Amit Sheth, Pramod Anantharam, Krishnaprasad Thirunarayan, "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Workshop on Personal Data Analytics in the Internet of Things at VLDB2014, Hangzhou, China, September 5, 2014.
Accompanying Video: http://youtu.be/pqcbwGYHPuc
Paper: http://www.knoesis.org/library/resource.php?id=2008
kHealth: Semantic Multi-sensory Mobile Approach to Personalized Asthma CareAmit Sheth
P7: A New Paradigm for Health Care in the 21st Century
Scientific Session at AAAS2019 Annual Meeting
https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/21133
https://cra.org/ccc/ccc-at-aaas/2019-sessions/
Asthma is a chronic multifactorial disease and traditional clinical practice requires patients to meet their clinician in a timely yet infrequently meetings scheduled once in 3-6 months depending on the patient’s condition. The clinical diagnosis relies on the patient’s description of their current health condition. The patient’s description need not be accurate at times and may lack some important aspects needed for accurate diagnosis. We at Kno.e.sis work with clinicians and their pediatric asthma patients at the Dayton Children's Hospital to evaluate an IoT/mobileApp enabled personalized digital health management. We built a kHealth system for continuous monitoring and improved tracking of 30 parameters including the child’s symptoms, activities, sleep, and treatment adherence. It can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness.
More at: https://aaas.confex.com/aaas/2019/meetingapp.cgi/Paper/23000
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Amit Sheth
Keynote at On the Move conference, October 2011, Greece.
Abstract:
Traditionally, we had to artificially simplify the complexity and richness of the real world to constrained computer models and languages for more efficient computation. Today, devices, sensors, human-in-the-loop participation and social interactions enable something more than a “human instructs machine” paradigm. Web as a system for information sharing is being replaced by pervasive computing with mobile, social, sensor and devices dominated interactions. Correspondingly, computing is moving from targeted tasks focused on improving efficiency and productivity to a vastly richer context that support events and situational awareness, and enrich human experiences encompassing recognition of rich sets of relationships, events and situational awareness with spatio-temporal-thematic elements, and socio-cultural-behavioral facets. Such progress positions us for what I call an emerging era of “computing for human experience” (CHE). Four of the key enablers of CHE are: (a) bridging the physical/digital (cyber) divide, (b) elevating levels of abstractions and utilizing vast background knowledge to enable integration of machine and human perception, (c) convert raw data and observations, ranging from sensors to social media, into understanding of events and situations that are meaningful to humans, and (d) doing all of the above at massive scale covering the Web and pervasive computing supported humanity. Semantic Web (conceptual models/ontologies and background knowledge, annotations, and reasoning) techniques and technologies play a central role in important tasks such as building context, integrating online and offline interactions, and help enhance human experience in their natural environment.
In this talk I will discuss early enablers of CHE including semantics-empowered social networking and sensor Web, and computation of higher level abstractions from raw and phenomenological data. An article in IEEE Internet Computing provides background information: http://bit.ly/HumanExperience
Keynote at: https://www.springer.com/us/book/9783642251054
Event Date: Oct 18, 2011
Augmented Personalized Health: dHealth approach to patient empowerment for ma...Amit Sheth
Web site: https://aihealth.ischool.utexas.edu/AIHealthWWW2021/index.html
Amit Sheth, Keynote at the International Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at The Web Conference 2021, 16 April 2021.
Abstract:
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions. The exploitation of all relevant data, relevant medical knowledge, and AI techniques will extend and enhance human health and well-being.
Augmented Personalized Healthcare (APH) strategy as we have defined involves empowering patients with self-monitoring (collecting relevant data), self-appraisal (interpreting data in the patient's context), self-management (assisting the patient in following personalized care plan to maintain health), to intervention (when the clinical help is needed) and disease progression tracking and prediction (http://bit.ly/AI-APH, http://bit.ly/APH-TED). While we have early investigations for several diseases, we will share some experience (such as developing a digital phenotype) from pediatric asthma that involved an evaluation with ~200 patients (http://bit.ly/kAsthma).
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...Amit Sheth
https://sites.google.com/view/deep-dial-2019/keynotes
Understanding and managing health is complex. Throughout the last few decades of modern medicine, we have relied clinicians on most health-related decision making. New technologies have enabled a growing involvement of patients in their own health management, aided by increasing variety and amount of patient-generated health data. Augmented personalized health [http://bit.ly/k-APH, http://bit.ly/APH-HI] strategy has outlined a broad variety of patient and clinician engagement in devising an increasingly more sophisticated and powerful health management solutions - from self-monitoring, self-appraisal, self-management, intervention to the prediction of disease progression and planning. Chatbot could play a pivotal role throughout the unfolding data-driven, AI-supported ecosystem [http://bit.ly/H-Chatbot] that engages patients and clinicians in collecting data, in driving their actions, informing them of their choices, and even delivering part of the clinical care (e.g., Cognitive Behavioral Therapy (CBT) for mental health patients). Nevertheless, this will require quite a few advances in making a more intelligent technology. In this talk, we will share some experience and observations based on our ongoing collaborative projects that usually involve clinicians and patients targeting pediatric asthma management, pre-and-post bariatric surgery care regimen, depression and other mental health issues, and nutrition. Using use cases and prototypes, we will elucidate the need, support, and use of domain- and user-specific knowledge graphs, Natural Language Processing (NLP), machine learning, and conversational AI for:
- multimodal interactions including text, voice, and other media, along with the use of diverse devices and software platforms for “natural” communication
- context enabled by deep relevant medical/healthcare knowledge including clinical protocols
- personalization by collecting and using the history of the individual patient from IoT health devices, open data, and Electronic Medical Record (EMR)
- abstraction by aggregating and correlating diverse streams data to draw plausible explanation(s) based on public (cohort-level) data (for example percentage of asthmatic patient who gets symptom when exposed to certain triggers) and personal data
- smart dialogue (intent) management and response generations by causal relations and inference of association
Startups Step Up - how healthcare ai startups are taking action during covid-...Renee Yao
All around the world, people are facing unprecedented challenges and uncertainties as a result of COVID-19. At NVIDIA Inception program, a virtual incubation startup program, which hosts 5000+ AI startups, we see an army of healthcare AI startups that have mobilized to address this global health crisis. This webinar will share real world examples on how each offering plays a critical role during this pandemic.
Live event: https://www.meetup.com/Women-in-Big-Data-Meetup/events/270191555/?action=rsvp&response=3.
YouTube Link: https://www.youtube.com/watch?v=QWkKINi8u4o&feature=youtu.be
Background on the 30 projects pitching at the DayOne Conference on 9th September 2019. At the conference the projects will be assisted by mentors and conference participants to create a journey map to help them on their path to healthcare innovation.
kHealth: Semantic Multi-sensory Mobile Approach to Personalized Asthma CareAmit Sheth
P7: A New Paradigm for Health Care in the 21st Century
Scientific Session at AAAS2019 Annual Meeting
https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/21133
https://cra.org/ccc/ccc-at-aaas/2019-sessions/
Asthma is a chronic multifactorial disease and traditional clinical practice requires patients to meet their clinician in a timely yet infrequently meetings scheduled once in 3-6 months depending on the patient’s condition. The clinical diagnosis relies on the patient’s description of their current health condition. The patient’s description need not be accurate at times and may lack some important aspects needed for accurate diagnosis. We at Kno.e.sis work with clinicians and their pediatric asthma patients at the Dayton Children's Hospital to evaluate an IoT/mobileApp enabled personalized digital health management. We built a kHealth system for continuous monitoring and improved tracking of 30 parameters including the child’s symptoms, activities, sleep, and treatment adherence. It can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness.
More at: https://aaas.confex.com/aaas/2019/meetingapp.cgi/Paper/23000
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Amit Sheth
Keynote at On the Move conference, October 2011, Greece.
Abstract:
Traditionally, we had to artificially simplify the complexity and richness of the real world to constrained computer models and languages for more efficient computation. Today, devices, sensors, human-in-the-loop participation and social interactions enable something more than a “human instructs machine” paradigm. Web as a system for information sharing is being replaced by pervasive computing with mobile, social, sensor and devices dominated interactions. Correspondingly, computing is moving from targeted tasks focused on improving efficiency and productivity to a vastly richer context that support events and situational awareness, and enrich human experiences encompassing recognition of rich sets of relationships, events and situational awareness with spatio-temporal-thematic elements, and socio-cultural-behavioral facets. Such progress positions us for what I call an emerging era of “computing for human experience” (CHE). Four of the key enablers of CHE are: (a) bridging the physical/digital (cyber) divide, (b) elevating levels of abstractions and utilizing vast background knowledge to enable integration of machine and human perception, (c) convert raw data and observations, ranging from sensors to social media, into understanding of events and situations that are meaningful to humans, and (d) doing all of the above at massive scale covering the Web and pervasive computing supported humanity. Semantic Web (conceptual models/ontologies and background knowledge, annotations, and reasoning) techniques and technologies play a central role in important tasks such as building context, integrating online and offline interactions, and help enhance human experience in their natural environment.
In this talk I will discuss early enablers of CHE including semantics-empowered social networking and sensor Web, and computation of higher level abstractions from raw and phenomenological data. An article in IEEE Internet Computing provides background information: http://bit.ly/HumanExperience
Keynote at: https://www.springer.com/us/book/9783642251054
Event Date: Oct 18, 2011
Augmented Personalized Health: dHealth approach to patient empowerment for ma...Amit Sheth
Web site: https://aihealth.ischool.utexas.edu/AIHealthWWW2021/index.html
Amit Sheth, Keynote at the International Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at The Web Conference 2021, 16 April 2021.
Abstract:
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions. The exploitation of all relevant data, relevant medical knowledge, and AI techniques will extend and enhance human health and well-being.
Augmented Personalized Healthcare (APH) strategy as we have defined involves empowering patients with self-monitoring (collecting relevant data), self-appraisal (interpreting data in the patient's context), self-management (assisting the patient in following personalized care plan to maintain health), to intervention (when the clinical help is needed) and disease progression tracking and prediction (http://bit.ly/AI-APH, http://bit.ly/APH-TED). While we have early investigations for several diseases, we will share some experience (such as developing a digital phenotype) from pediatric asthma that involved an evaluation with ~200 patients (http://bit.ly/kAsthma).
Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Sem...Amit Sheth
https://sites.google.com/view/deep-dial-2019/keynotes
Understanding and managing health is complex. Throughout the last few decades of modern medicine, we have relied clinicians on most health-related decision making. New technologies have enabled a growing involvement of patients in their own health management, aided by increasing variety and amount of patient-generated health data. Augmented personalized health [http://bit.ly/k-APH, http://bit.ly/APH-HI] strategy has outlined a broad variety of patient and clinician engagement in devising an increasingly more sophisticated and powerful health management solutions - from self-monitoring, self-appraisal, self-management, intervention to the prediction of disease progression and planning. Chatbot could play a pivotal role throughout the unfolding data-driven, AI-supported ecosystem [http://bit.ly/H-Chatbot] that engages patients and clinicians in collecting data, in driving their actions, informing them of their choices, and even delivering part of the clinical care (e.g., Cognitive Behavioral Therapy (CBT) for mental health patients). Nevertheless, this will require quite a few advances in making a more intelligent technology. In this talk, we will share some experience and observations based on our ongoing collaborative projects that usually involve clinicians and patients targeting pediatric asthma management, pre-and-post bariatric surgery care regimen, depression and other mental health issues, and nutrition. Using use cases and prototypes, we will elucidate the need, support, and use of domain- and user-specific knowledge graphs, Natural Language Processing (NLP), machine learning, and conversational AI for:
- multimodal interactions including text, voice, and other media, along with the use of diverse devices and software platforms for “natural” communication
- context enabled by deep relevant medical/healthcare knowledge including clinical protocols
- personalization by collecting and using the history of the individual patient from IoT health devices, open data, and Electronic Medical Record (EMR)
- abstraction by aggregating and correlating diverse streams data to draw plausible explanation(s) based on public (cohort-level) data (for example percentage of asthmatic patient who gets symptom when exposed to certain triggers) and personal data
- smart dialogue (intent) management and response generations by causal relations and inference of association
Startups Step Up - how healthcare ai startups are taking action during covid-...Renee Yao
All around the world, people are facing unprecedented challenges and uncertainties as a result of COVID-19. At NVIDIA Inception program, a virtual incubation startup program, which hosts 5000+ AI startups, we see an army of healthcare AI startups that have mobilized to address this global health crisis. This webinar will share real world examples on how each offering plays a critical role during this pandemic.
Live event: https://www.meetup.com/Women-in-Big-Data-Meetup/events/270191555/?action=rsvp&response=3.
YouTube Link: https://www.youtube.com/watch?v=QWkKINi8u4o&feature=youtu.be
Background on the 30 projects pitching at the DayOne Conference on 9th September 2019. At the conference the projects will be assisted by mentors and conference participants to create a journey map to help them on their path to healthcare innovation.
Background on the 30 projects pitching at the DayOne Conference on 9th September 2019. At the conference the projects will be assisted by mentors and conference participants to create a journey map to help them on their path to healthcare innovation.
Augmented Personalized Health: an explicit knowledge enhanced neurosymbolic d...Amit Sheth
Keynote at the SWAT4HCLS (Semantic Web Applications and Tools for Healthcare and Life Sciences), 12 Jan 2022. Event info:
http://www.swat4ls.org/workshops/leiden2022/keynotes/
Video: https://youtu.be/nwGAv9q2wsY
Healthcare as we know it is in the process of going through a massive change – from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions. The exploitation of all relevant data, relevant medical knowledge, and explainable AI techniques will also support better communications between patients, clinicians, and virtual health assistants with higher-level abstractions (rather than low-level data) representing health choices, decisions and actions.
Augmented Personalized Healthcare (APH) strategy we are developing empowers patients with self-monitoring (collecting relevant data), self-appraisal (interpreting data in the patient’s context), self-management (assisting the patient in following personalized care plan to maintain health), to intervention (when the clinical help is needed) and disease progression tracking and prediction (http://bit.ly/AI-APH, http://bit.ly/APH-TED). We currently apply APH using mobile Apps and virtual health assistants for patients managing pediatric asthma (http://bit.ly/kAsthma), mental health, carbohydrate management for type 1 diabetes, hypertension, etc. In this talk, I will describe some of the technical components that incorporate context, personalization, and abstraction for supporting advanced capabilities such as patient engagement through meaningful question generation, chatbot safety, and explainable decision-making using knowledge-infused learning, a neurosymbolic AI strategy that utilizes many types and levels of explicit knowledge.
Gleecus Whitepaper : Applications of Artificial Intelligence in HealthcareSuprit Patra
In the field of medicine, Artificial Intelligence (AI) goes a long way in strengthening and improvising the communication between Doctors and Patient like never before. The Healthcare industry requires enormous amounts of digitized data to be periodically shared, stored and yet kept secure at the same time. Smart algorithms are powering artificial intelligence (AI) applications in the healthcare sector By enabling intelligent applications to not only speak and listen but also to make decisions in unrivaled ways to nullify human errors.
Read this research paper to know how AI is taking healthcare by storm.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how mobile devices are becoming more economically feasible for health care. Rapid improvements in electronics are enabling a wide variety of health-related attachments to become available for mobile phones. These attachments can analyze breath, blood oxygen levels, blood glucose, blood type, and urine and do ultrasounds. These advances will change the way health care is monitored and managed.
Promise and peril: How artificial intelligence is transforming health careΔρ. Γιώργος K. Κασάπης
AI has enormous potential to improve the quality of health care, enable early diagnosis of diseases, and reduce costs. But if implemented incautiously, AI can exacerbate health disparities, endanger patient privacy, and perpetuate bias. STAT, with support from the Commonwealth Fund, explored these possibilities and pitfalls during the past year and a half, illuminating best practices while identifying concerns and regulatory gaps. This report includes many of the articles we published and summarizes our findings, as well as recommendations we heard from caregivers, health care executives, academic experts, patient advocates, and others.
Life expectancy has increased greatly over the past 100 years. Increased wealth, sanitation, and access to pharmaceutical innovation have contributed to our health, allowing us to live longer and healthier lives. We are on a tipping-point of healthcare mostly related to new technologies - big data and genomics, robotics, immunotherapy, remote monitoring, 3D printing, among others, will bring forth a new era in health and standards of care.
May 2021 snapshot of some of the Research and Collaborations in dHealth/personalized health, public health, epidemiology, biomedicine at the AI Institute of the University of South Carolina [AIISC]
Cloud Computing: A Key to Effective & Efficient Disease Surveillance Systemidescitation
Cloud computing, a future generation concept
characterized by three entities: Software, hardware &
network designed to enhance the capacity building
simultaneously increasing the throughput by extending the
reach for any system without having heavy investment of
infrastructure and training new personnel. It is becoming
a major building block for any sort of businesses across the
globe. This paper likes to propose a cloud as a solution for
having an effective disease surveillance system. Till now,
multiple surveillance systems come into play but still they
lack sensitivity, specificity & timeliness.
HIPAA Security Rule Compliance When Communicating with Patients Using Mobile ...Project HealthDesign
This webinar, held Jan. 26, 2011, served to inform and engage the five current Project HealthDesign teams around legal and policy topics involved when clinicians communicate with patients via mobile devices.
Krishnaprasad Thirunarayan and Amit Sheth: Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications, In: Proceedings of AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013.
With the rapid proliferation of mobile phones, social media, and sensors, it is critical to collect and convert big data so generated into actionable information that is relevant for decision making. In this session, we explore challenges and approaches for synthesizing relevant background knowledge and inferences that can enable smart healthcare and ultimately benefit community at large.
Paper: http://www.knoesis.org/library/resource.php?id=1903
Presentation given by Chris Welty (IBM Research) at Knoesis. We get the permission to upload this presentation from Chris Welty. Event details are at: http://j.mp/Welty-at-Knoesis and the associate video is at: https://www.youtube.com/watch?v=grDKpicM5y0
Background on the 30 projects pitching at the DayOne Conference on 9th September 2019. At the conference the projects will be assisted by mentors and conference participants to create a journey map to help them on their path to healthcare innovation.
Augmented Personalized Health: an explicit knowledge enhanced neurosymbolic d...Amit Sheth
Keynote at the SWAT4HCLS (Semantic Web Applications and Tools for Healthcare and Life Sciences), 12 Jan 2022. Event info:
http://www.swat4ls.org/workshops/leiden2022/keynotes/
Video: https://youtu.be/nwGAv9q2wsY
Healthcare as we know it is in the process of going through a massive change – from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions. The exploitation of all relevant data, relevant medical knowledge, and explainable AI techniques will also support better communications between patients, clinicians, and virtual health assistants with higher-level abstractions (rather than low-level data) representing health choices, decisions and actions.
Augmented Personalized Healthcare (APH) strategy we are developing empowers patients with self-monitoring (collecting relevant data), self-appraisal (interpreting data in the patient’s context), self-management (assisting the patient in following personalized care plan to maintain health), to intervention (when the clinical help is needed) and disease progression tracking and prediction (http://bit.ly/AI-APH, http://bit.ly/APH-TED). We currently apply APH using mobile Apps and virtual health assistants for patients managing pediatric asthma (http://bit.ly/kAsthma), mental health, carbohydrate management for type 1 diabetes, hypertension, etc. In this talk, I will describe some of the technical components that incorporate context, personalization, and abstraction for supporting advanced capabilities such as patient engagement through meaningful question generation, chatbot safety, and explainable decision-making using knowledge-infused learning, a neurosymbolic AI strategy that utilizes many types and levels of explicit knowledge.
Gleecus Whitepaper : Applications of Artificial Intelligence in HealthcareSuprit Patra
In the field of medicine, Artificial Intelligence (AI) goes a long way in strengthening and improvising the communication between Doctors and Patient like never before. The Healthcare industry requires enormous amounts of digitized data to be periodically shared, stored and yet kept secure at the same time. Smart algorithms are powering artificial intelligence (AI) applications in the healthcare sector By enabling intelligent applications to not only speak and listen but also to make decisions in unrivaled ways to nullify human errors.
Read this research paper to know how AI is taking healthcare by storm.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how mobile devices are becoming more economically feasible for health care. Rapid improvements in electronics are enabling a wide variety of health-related attachments to become available for mobile phones. These attachments can analyze breath, blood oxygen levels, blood glucose, blood type, and urine and do ultrasounds. These advances will change the way health care is monitored and managed.
Promise and peril: How artificial intelligence is transforming health careΔρ. Γιώργος K. Κασάπης
AI has enormous potential to improve the quality of health care, enable early diagnosis of diseases, and reduce costs. But if implemented incautiously, AI can exacerbate health disparities, endanger patient privacy, and perpetuate bias. STAT, with support from the Commonwealth Fund, explored these possibilities and pitfalls during the past year and a half, illuminating best practices while identifying concerns and regulatory gaps. This report includes many of the articles we published and summarizes our findings, as well as recommendations we heard from caregivers, health care executives, academic experts, patient advocates, and others.
Life expectancy has increased greatly over the past 100 years. Increased wealth, sanitation, and access to pharmaceutical innovation have contributed to our health, allowing us to live longer and healthier lives. We are on a tipping-point of healthcare mostly related to new technologies - big data and genomics, robotics, immunotherapy, remote monitoring, 3D printing, among others, will bring forth a new era in health and standards of care.
May 2021 snapshot of some of the Research and Collaborations in dHealth/personalized health, public health, epidemiology, biomedicine at the AI Institute of the University of South Carolina [AIISC]
Cloud Computing: A Key to Effective & Efficient Disease Surveillance Systemidescitation
Cloud computing, a future generation concept
characterized by three entities: Software, hardware &
network designed to enhance the capacity building
simultaneously increasing the throughput by extending the
reach for any system without having heavy investment of
infrastructure and training new personnel. It is becoming
a major building block for any sort of businesses across the
globe. This paper likes to propose a cloud as a solution for
having an effective disease surveillance system. Till now,
multiple surveillance systems come into play but still they
lack sensitivity, specificity & timeliness.
HIPAA Security Rule Compliance When Communicating with Patients Using Mobile ...Project HealthDesign
This webinar, held Jan. 26, 2011, served to inform and engage the five current Project HealthDesign teams around legal and policy topics involved when clinicians communicate with patients via mobile devices.
Krishnaprasad Thirunarayan and Amit Sheth: Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications, In: Proceedings of AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013.
With the rapid proliferation of mobile phones, social media, and sensors, it is critical to collect and convert big data so generated into actionable information that is relevant for decision making. In this session, we explore challenges and approaches for synthesizing relevant background knowledge and inferences that can enable smart healthcare and ultimately benefit community at large.
Paper: http://www.knoesis.org/library/resource.php?id=1903
Presentation given by Chris Welty (IBM Research) at Knoesis. We get the permission to upload this presentation from Chris Welty. Event details are at: http://j.mp/Welty-at-Knoesis and the associate video is at: https://www.youtube.com/watch?v=grDKpicM5y0
A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is
increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
Amit Sheth, 'Semantic Computing in Real-World: Vertical and Horizontal application, within Enterprise and on the Web, ' Panel Presentation at International Conference on Semantic Computing (ICSC2011), Palo Alto, CA, September 20, 2011.
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, and Amit Sheth, 'Trust Networks', In: 5th Indian International Conference on Artificial Intelligence (IICAI-11), December 14-16, 2011 (invited tutorial).
Amit Sheth, "Semantic Interoperability and Information Brokering in Global Information Systems," Keynote given at IEEE Meta-Data, Bathesda, MD, April 6 1999.
Talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001.
More at: http://wiki.knoesis.org/index.php/SSW
Dissertation Advisor: Prof. Amit Sheth
Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people’s curs- ing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform – Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding curs- ing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing.
Original paper: http://knoesis.org/library/resource.php?id=1937
Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit Sheth, User Interests Identification on Twitter Using a Hierarchical Knowledge Base, ESWC 2014, May 2014.
Paper at: http://j.mp/user-ig
More at: http://wiki.knoesis.org/index.php/Hierarchical_Interest_Graph
Invited talk presented by Hemant Purohit (http://knoesis.org/researchers/hemant) at the NCSU workshop on IT for sustainable tourism development. The talk presents application of technology developed for crisis coordination into more general marketplace coordination via social media for helping suppliers (micro-entrepreneurs) and demanders (tourists).
The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
Paper: http://bit.ly/k-APH Video: https://youtu.be/wDi1mLLyxuc
Invited talk: @ Big Data Integration and IoT for Smart Health Care,3rd Intl Forum on Research and Technologies for Society and Industry Modena Italy, 13 September 2017
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
2016 SXSW Interactive Panel Submission:
Big Data and mobile health are transforming the healthcare landscape. The digital revolution is here and health tech heavyweights are ready to ride the wave. Take a look into the future of drug development and how mHealth technology, including wearables, sensors and apps, are uncovering new “digital biomarkers” and driving a patient-centric research model. The connected patient paired with tech straight out of the Matrix is altering the way we’re collecting and understanding our own data, helping people to better understand themselves today to proactively lead healthier lives tomorrow.
Presented at the Expert Panel Discussion: The Future of Telehealth Technology at National Telehealth Conference, 10 Oct 2017, Cincinnati: http://www.nationaltelehealthconference.com
This is an abridged version of an invited talk: https://youtu.be/wDi1mLLyxuc
Big Data, CEP and IoT : Redefining Healthcare Information Systems and AnalyticsTauseef Naquishbandi
Big Data is a term encompassing the use of techniques to capture, process, analyze and visualize potentially large datasets in a reasonable time frame not accessible to standard technologies.
It refers to the ability to crunch vast collections of information, analyze it instantly, and draw from it sometimes profoundly surprising conclusions
Big data solutions can help stakeholders personalize care, engage patients, reduce variability and costs, and improve quality of health delivery.
Big data analytics can also contribute to providing a rich context to shape many areas of health care like analysis of effects, side-effects of drugs, genome analysis etc.
The Personalized Health Risk Profile: A New Tool for Safety and Occupational ...Richard Hartman, Ph.D.
This presentation introduces the Personalized Risk Health Profile (PRHP), a mathematical process to quantitatively evaluate personalized health risks by integrating workplace, lifestyle, and environmental exposure (the root cause of disease) data from traditional and new personal monitoring technologies combined with individual health histories and genomic data to provide a new and novel capability for the safety and health professionals and policymakers. The PRHP creates for the first time a mechanism to better understand the relationships between a worker's health, genetic predispositions, and exposures through mathematical expression and process, ultimately providing a modern tool to better understand the effects of exposures from the workplace, environment, as well as day-to-day activities. More importantly, the PRRP displays individual and population risks through user-friendly visualizations bridging the gap between "Population Health" and "Personalized Medicine" so safety and health professionals can recommend data-driven interventions to mitigate individual risks to improve health/performance, and policymakers and decision-makers can make more informed policy and resource decisions.
As global IH/OH professionals, we are positioned to effectively contribute as exposure scientists not only to the occupational health of individuals but to their overall well-being. This education session will demonstrate how the United States Air Force is ushering in a bold solution to capture workplace, environmental, and lifestyle exposures to the individual using advances in science, technology, and informatics called Total Exposure Health (TEH). TEH provides a framework and tools to strengthen prevention and reduce illness and injury through effective early intervention, improved health-related risk assessment decision-making, and risk mitigation. Individual Exposure Health Risk Profiles (IEHRP) attempt to quantitatively evaluate individual health risks based on genetics, occupational, lifestyle, and environmental exposures, medical disposition, protective factors,etc. Participants will have a new view of population-based standards and will explore the potential future of individual occupational exposure standards.
Day one conference projects with journey mapsDayOne
DayOne Conference 2019 projects and journey maps. 30 ventures presented their solutions and together with conference participants built a journey map of their future.
Real Time Health Monitoring System: A Reviewijtsrd
Generally in critical case patients are supposed to be monitored continuously for their heart rate, oxygen saturation level, blood pressure, body temperature, pulse-oximetry (SPO2) and ECG etc. In the previous methods, the doctors need to be present physically on sight, so that the real time health monitoring system is used every field such as hospital, home care unit, sports using wireless sensor network. This health monitoring system use for chronicle diseases patients who have daily check-up. So, researchers design a system as portable device. Researcher designed different health monitoring system based on requirement. Different platform like Microcontroller, ASIC, PIC microcontroller and embedded systems are used to design the system based on this performance and in the recent years cloud based e-healthcare systems have emerged. In future FPGA based or using IoT we can develop a system which will help to monitor different health parameters. Ajinkya Anant Bandegiri | Pradip Chandrakant Bhaskar"Real Time Health Monitoring System: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7092.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7092/real-time-health-monitoring-system-a-review/ajinkya-anant-bandegiri
The Future of mHealth - Jay Srini - March 2011LifeWIRE Corp
Jay Srini's presentation of her take on the Future of mHealth, presented at the 3rd mHealth Networking Conference, March 30, 2011. Aside from being one of the preeminent thought leader in the area of innovation and mhealth, she holds a number of positions including Assistant Professor at the University of Pittsburgh and CIO for LifeWIRE Corp.
Similar to kHealth: Proactive Personalized Actionable Information for Better Healthcare (20)
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
kHealth: Proactive Personalized Actionable Information for Better Healthcare
1. kHealth: Proactive Personalized Actionable Information for
Better Healthcare
Put Knoesis Banner
PDA@IoT, in conjunction with VLDB, September, 2014
Amit Sheth, Pramod Ananthram, T.K. Prasad
The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Wright State, USA
2. 2
A Historical Perspective on Collecting Health Observations
Imhotep
Laennec’s stethoscope
Image Credit: British Museum
2600 BC ~1815 Today
Diseases treated only
by external observations
First peek beyond just
external observations
Information overload!
Doctors relied only on
external observations
Stethoscope was the
first instrument to go
beyond just external
observations
Though the stethoscope
has survived, it is only one
among many observations
in modern medicine
http://en.wikipedia.org/wiki/Timeline_of_medicine_and_medical_technology
3. “The next wave of dramatic Internet growth will come through the confluence of
people, process, data, and things — the Internet of Everything (IoE).”
- CISCO IBSG, 2013
Beyond the IoE based infrastructure, it is the possibility of developing applications that spans
Physical, Cyber and the Social Worlds that is very exciting.
3
http://www.cisco.com/web/about/ac79/docs/innov/IoE_Economy.pdf
What has changed now?
4. ‘OF human’ : Relevant Real-time Data Streams for Human Experience
Petabytes of Physical(sensory)-Cyber-Social Data everyday!
More on PCS Computing: http://wiki.knoesis.org/index.php/PCS
4
5. 6
MIT Technology Review, 2012
The Patient of the Future
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
6. ‘FOR human’ : Improving Human Experience (Smart Health)
Weather Application
Asthma Healthcare
Application
Personal
Public Health
Detection of events, such as wheezing
sound, indoor temperature, humidity,
dust, and CO level
Close the window at home
during day to avoid CO in
gush, to avoid asthma attacks
at night
7
Population Level
Action in the Physical World
Luminosity
CO level
CO in gush
during day time
7. ‘FOR human’ : Improving Human Experience (Smart Energy)
Weather Application
Power Monitoring
Application
Personal Level Observations
Electricity usage over a day, device at
work, power consumption, cost/kWh,
heat index, relative humidity, and public
events from social stream
8
Population Level Observations
Action in the Physical World
Washing and drying has
resulted in significant cost
since it was done during peak
load period. Consider
changing this time to night.
8. kHealth
Knowledge-enabled Healthcare
Four current applications:
To reduce preventable readmissions of patients with
ADHF and GI; Asthma in children; patients with Dementia
9
10. Empowering Individuals (who are not Larry Smarr!) for their own health
Through physical monitoring and
analysis, our cellphones could act as
an early warning system to detect
serious health conditions, and
provide actionable information
canary in a coal mine
kHealth: knowledge-enabled healthcare
11
11. What?
• kHealth is a knowledge-based
approach/application for patient-centric
health-care that exploits:
(a) Web based tools and social media,
(b) Mobile phone technology and wireless sensors,
(c) For synthesizing personalized actions from
heterogeneous health data
(i) For disease prevention and treatment
(ii) For health, fitness and well-being
12
13. kHealth Kit for the application for Asthma management
Sensordrone
(Carbon monoxide,
temperature, humidity)
Node Sensor
(exhaled Nitric
Oxide)
15
Sensors
Android Device
(w/ kHealth App)
Total cost: ~ $500
*Along with two sensors in the kit, the application uses a variety of population level signals from the web:
Pollen level Air Quality
Temperature & Humidity
14. Why?
• “Unintelligible” health data deluge due to
– Continuous monitoring of patients using passive and
active sensors
– Continuous monitoring of environment using sensors
– Public health reports
– Population level information
– Social media conversations
– Personal Electronic Medical Records (EMRs)
– Wide use of affordable mobile/wireless technologies
19
15. Why?
• Empowering patients to improve health by
– Abstracting and integrating low-level sensor data
to more meaningful health signals
– Recommending personalized actions
• Ubiquitous, timely and effective health
management and telemedicine
– Involve patient and health-care team without
causing “interaction fatigue”
20
16. kHealth: Health Signal Processing Architecture
Personal level
Signals
Public level
Signals
Population level
Signals
Domain
Knowledge
Risk Model
Events from
Social Streams
Take Medication before
going to work
Contact doctor
Avoid going out in the
evening due to high pollen
levels
Analysis
Personalized
Actionable
Information
Data Acquisition &
aggregation
21
17. How?
• Data collection from various sources
– Active and passive sensing devices
– Social media crawling
– EMR
• Syntactic and semantic integration
– Qualitative/imprecise citizen observations
– Quantitative/precise sensor observations
• Provide complementary and collaborative information
• Using Semantic Web technologies, e.g., SemSOS
22
18. How?
• Semantic Perception: Reasoning for decision
making and action generation
– Perception cycle
– Personalized action recommendation using
• Patient health score (linear scale, RYG-abstraction)
• Patient vulnerability score (personalization)
– Qualify vs quantify
• Domain (e.g. disease) specific knowledge
23
19. 24
Asthma Domain Knowledge
Asthma Control
and Actionable Information
Domain
Knowledge
Asthma Control
à
Daily Medication
Choices for starting
therapy
Not Well Controlled Poor Controlled
Severity Level
of Asthma
(Recommended Action) (Recommended Action) (Recommended Action)
Intermittent Asthma SABA prn - -
Mild Persistent Asthma Low dose ICS Medium ICS Medium ICS
Moderate Persistent
Asthma
Medium dose ICS alone
Or with
LABA/montelukast
Medium ICS +
LABA/Montelukast
Or High dose ICS
Medium ICS +
LABA/Montelukast
Or High dose ICS*
Severe Persistent Asthma High dose ICS with
LABA/montelukast
Needs specialist care Needs specialist care
ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ;
*consider referral to specialist
20. 25
Patient Health Score (diagnostic)
How controlled is my asthma?
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
GREEN -- Well Controlled
YELLOW – Not well controlled
Red -- poor controlled
21. 26
Patient Vulnerability Score (prognostic)
How vulnerable* is my control level today?
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
Patient health
Score
*considering changing environmental conditions and current control level
22. Background
Knowledge
31
Health Signal Extraction to Understanding
Physical-Cyber-Social System Observations Health Signal Extraction Health Signal Understanding
Personal
Population Level
Acceleration readings from
on-phone sensors
Wheeze – Yes
Do you have tightness of chest? –Yes
Risk Category assigned by
doctors
<Wheezing=Yes, time, location>
<ChectTightness=Yes, time, location>
<PollenLevel=Medium, time, location>
<Pollution=Yes, time, location>
<Activity=High, time, location>
PollenLevel
Wheezing
ChectTightness
Pollution
Activity
PollenLevel
Wheezing
ChectTightness
Pollution
Activity
RiskCategory
<PollenLevel, ChectTightness, Pollution,
Activity, Wheezing, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
.
.
.
Expert
Knowledge
Sensor and personal
observations
tweet reporting pollution level
and asthma attacks
Signals from personal, personal
spaces, and community spaces
Qualify
Quantify
Enrich
Outdoor pollen and pollution
Public Health
Well Controlled - continue
Not Well Controlled – contact nurse
Poor Controlled – contact doctor
23. 36
How are machines supposed to integrate and interpret sensor data?
RDF OWL
Semantic Sensor Networks (SSN)
24. 39
W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K.,
Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
25. 41
What if we could automate this sense making ability?
… and do it efficiently and at scale
26. SSN
Ontology
2 Interpreted data
(deductive)
[in OWL]
e.g., threshold
1 Annotated Data
[in RDF]
e.g., label
0 Raw Data
[in TEXT]
e.g., number
Levels of Abstraction
3 Interpreted data
(abductive)
[in OWL]
e.g., diagnosis
Intellego
Hyperthyroidism
… …
Elevated
Blood
Pressure
Systolic blood pressure of 150 mmHg
“150”
42
28. People are good at making sense of sensory input
What can we learn from cognitive models of perception?
• The key ingredient is prior knowledge
44
29. Semantic Perception : Perception Cycle
Semantic perception in kHealth involves:
• Abductive reasoning to derive candidate
explanations for sensor data, and
• Deductive reasoning to disambiguate among
multiple explanations with patient inputs and
additional targeted sensor observations.
Intellego
45
30. Observe
Property
* based on Neisser’s cognitive model of perception
Perceive
Feature
Explanation
Discrimination
1
2
Perception Cycle*
Translating low-level signals
into high-level knowledge
Focusing attention on those
aspects of the environment that
provide useful information
Prior Knowledge
46
31. To enable machine perception,
Semantic Web technology is used to integrate
sensor data with prior knowledge on the Web
47
32. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
48
33. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
49
34. Explanation is the act of choosing the objects or events that best account for a
set of observations; often referred to as hypothesis building
Observe
Property
Perceive
Feature
Explanation
1
Explanation
Translating low-level signals
into high-level knowledge
50
35. Discrimination is the act of finding those properties that, if observed, would help distinguish
between multiple explanatory features
Observe
Property
Perceive
Feature
Explanation
Discrimination
2
Focusing attention on those
aspects of the environment that
provide useful information
Discrimination
51
37. Semantic Perception : Abstraction
• Mapping low-level sensor values to coarse-grain
abstract values
– E.g., Blood pressure: 150/100 => High bp
• Extracting signatures for high-level human
comprehensible features from low-level
sensor data stream.
– E.g., Parkinson disease : unsteady walk, fall,
slurred speech, etc.
53
38. How do we implement machine perception efficiently on a
resource-constrained device?
Use of OWL reasoner is resource intensive
(especially on resource-constrained devices),
in terms of both memory and time
• Runs out of resources with prior knowledge >> 15 nodes
• Asymptotic complexity: O(n3)
57
39. Approach 1: Send all sensor observations
to the cloud for processing
Approach 2: downscale semantic
processing so that each device is capable
of machine perception
intelligence at the edge
Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,
ISWC 2012.
58
40. Efficient execution of machine perception
Use bit vector encodings and their operations to encode prior knowledge and
execute semantic reasoning
010110001101
0011110010101
1000110110110
101100011010
0111100101011
000110101100
0110100111
59
41. Evaluation on a mobile device
Efficiency Improvement
• Problem size increased from 10’s to 1000’s of nodes
• Time reduced from minutes to milliseconds
• Complexity growth reduced from polynomial to linear
O(n3) < x < O(n4) O(n)
60
42. Semantic Perception for smarter analytics: 3 ideas to takeaway
1 Translate low-level data to high-level knowledge
Machine perception can be used to convert low-level sensory
signals into high-level knowledge useful for decision making
2 Prior knowledge is the key to perception
Using SW technologies, machine perception can be formalized and
integrated with prior knowledge on the Web
3 Intelligence at the edge
By downscaling semantic inference, machine perception can
execute efficiently on resource-constrained devices
61
43. 62
thank you, and please visit us at
http://knoesis.org
Editor's Notes
Starting slide
Various Big data problems – Traditional examples vs what we are doing examples.
Variety and Velocity than Volume. kHealth problem. People will be interested in Smart Data.
Traditional ML techniques, High Performance Computing, Statistics. Human level of Abstraction is Smart data.
"2600 BC – Imhotep wrote texts on ancient Egyptian medicine describing diagnosis and treatment of 200 diseases in 3rd dynasty Egypt.”
Sir William Osler, 1st Baronet, was a Canadian physician and one of the four founding professors of Johns Hopkins Hospital. He was called the father of modern medicine. Sir William Osler called Imhotep as the true father of medicine.
There are over 99.4% of physical devices that may one day be connected to
The Internet still unconnected.
- CISCO IBSG, 2013
All the data related to human activity, existence and experiences
More on PCS Computing: http://wiki.knoesis.org/index.php/PCS
TKP: Should not knowledge be used to bridge the gap between data and decision and action?
Or are we saying we need to glean knowledge?
- Larry Smarr is a professor at the University of California, San Diego
And he was diagnosed with Chrones Disease
What’s interesting about this case is that Larry diagnosed himself
He is a pioneer in the area of Quantified-Self, which uses sensors to monitor physiological symptoms
Through this process he discovered inflammation, which led him to discovery of Chrones Disease
This type of self-tracking is becoming more and more common
Actionable information example:
In Asthma use case we have a sensor – sensordrone which records luminosity and CO levels
A high correlation between CO level and luminosity is found
This is an actionable information to the user interpreting it as CO in gush during day time
=> Mitigating action can be “closing the window” during day
Also, we have weather application which performs abstraction on weather sensory observations to identify blizzard conditions (food for actions!!) :
-- 20,000 weather stations (with ~5 sensors per station)
-- Real-Time Feature Streams
- live demo: http://knoesis1.wright.edu/EventStreams/
- video demo: https://skydrive.live.com/?cid=77950e284187e848&sc=photos&id=77950E284187E848%21276
- With this ability, many problems could be solved
- For example: we could help solve health problems (before they become serious health problems) through monitoring symptoms and real-time sense making, acting as an early warning system to detect problematic health conditions
ADHF – Acute Decompensated Heart Failure
1)www.pollen.com(For pollen levels)
2)http://www.airnow.gov/(For air quality levels)
3)http://www.weatherforyou.com/(For temperature and humidity)
Data overload in the context of asthma
“
Research on Asthma has three phases
Data collection: what signals to collect?
Analysis: what analysis to be done?
Actionable information: what action to recommend?
In the next slide, we take a peek into the analysis that we do for Asthma
What is the current state of a person/patient? => Summarizing all the observations (sensor and personal) into a single score indicating health of a person
Instead of presenting all the raw data (often to much e.g., Asthma application we have developed collects CO, temperature, and humidity every 10 seconds resulting in 8,640 observations/day) which may not be comprehensible to the patient, we empower them by providing actionable summaries.
What is the likely state of the person in future? => Given the current state and the changing environmental conditions, estimate the state of the person by summarizing it into a number which is actionable.
For example, vulnerability score for a person with Asthma is computed with environmental factors (pollen, air quality, external temperature and humidity) and current state of the patient.
Intuitively, a person with well controlled asthma should have a lower vulnerability score than a person with poorly controlled asthma both being in a poor environmental state.
In the absence of declarative knowledge in a domain, we resort to statistical approaches to glean insights from data
Even if there is declarative knowledge of a domain, it may have to be personalized
The CO level may be related to the luminosity as observed by the sensordrone – as it gets brighter the CO level also increases => high CO level in daytime
If such an insight is provided to a person, the interpretation can be:
Some activity inside the house leads to high CO levels
Outside activity leads to high CO levels inside the house
Since the person knows that he/she is absent in the house during mornings, it has to be something from outside.
- Person narrows down to a possible opened window at home (forgot to close more often)
There are two components in making sense of Health Signals:
Health signal extraction – processing, aggregating, and abstracting from raw sensor/textual data to create human intelligible abstractions
Health signal understanding – derive (1) connections between abstractions and (2)
Action recommendation:
Continue
Contact nurse
Contact doctor
Only score based structure extraction is presented here. Other popular structure extraction techniques include constraint based approaches which finds independences between random variables X1, …, Xn
I-Map => different structures result in the same loglikelihood score. Thus recovering the original structure of the graph generating data using data alone is considered impossible! We go the the rescue of declarative knowledge to: (1) choose promising structures and (2) to break ties when two structure results in the same score
Massive amount of data is collected by sensors and mobile devices yet patients and doctors care about “actionable” information.
This data has all the four Vs of big data and we used knowledge enabled techniques to transform it into value
In the context of PD, we analyzed massive amount of sensor data collected by sensors on a smartphones to understand detection and characterization of PD severity.
- what if we could automate this sense making ability?
- and what if we could do this at scale?
sense making based on human cognitive models
perception cycle contains two primary phases
explanation
translating low-level signals into high-level abstractions
inference to the best explanation
discrimination
focusing attention on those properties that will help distinguish between multiple possible explanations
used to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
compute machine perception inferences -- i.e., explanation and discrimination -- of high-complexity on a resource-constrained devices in miliseconds
Difference between the other systems and what this system provides
Intelligence at the age. Shipping computation and domain models to the edge (Distributed)
More at: http://wiki.knoesis.org/index.php/PCS
And http://knoesis.org/projects/ssw/