A quick review of Kno.e.sis’ research subset on knowledge-enhanced learning with on personal and public health, wellbeing and social good applications.
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).
Social Media took over our lives in most different aspects. Even health care providers are becoming more aware of how the digital world and services, i.e. Apps, social Networks,... can be of benefit for them and for their visitors and patients.
4 Digital Health Trends Affecting Your Revenue CycleMeduit
The emerging digital trends impacting the healthcare industry are as varied as the new technologies being developed, but there are four trends that are having a more significant impact on the revenue cycle. Find out what they are in this Meduit Innovation Lab guide!
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).
Social Media took over our lives in most different aspects. Even health care providers are becoming more aware of how the digital world and services, i.e. Apps, social Networks,... can be of benefit for them and for their visitors and patients.
4 Digital Health Trends Affecting Your Revenue CycleMeduit
The emerging digital trends impacting the healthcare industry are as varied as the new technologies being developed, but there are four trends that are having a more significant impact on the revenue cycle. Find out what they are in this Meduit Innovation Lab guide!
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Personal connected health is currently characterized by limited thought leadership, insufficient coordination and collaboration, and a lack of awareness and understanding of the full potential by all stakeholders: public, providers, policymakers, industry and patients. The Personal Connected Health Alliance is defining the the field of personal connected health to inspire market and policy innovation, research and collective action for sustained adoption of personal connected health technology. The vision is better health and well being for all through increased personal responsibilities and connectivity as well as improved care delivery enabled by technology.
Collaborated with the Mayo Clinic's Centre for Innovation on a team project to envision a 2035 future for specialized healthcare providers. Researched trends and drivers from a social, technological, economic, political, environment and values perspective and applied strategic foresight/futures methods to create possible future outcomes. Designed strategies to influence a positive future and mitigate against negative outcomes. The final report was used by the clinic as an innovation input for their multi-year strategic planning activities.
The Future of Medical Education From Dreams to Reality (VR, AR, AI)SeriousGamesAssoc
With three decades of e-learning experience, Dr. Levy will present innovations in technology-enhanced education from the past, present, and into the future. He will highlight some of his medical education inventions and advances including some of the first laser discs, CD-ROMs, online case-based education, 3-D anatomical and procedural animations, robotic-assisted surgery, and virtual reality surgical simulation. He will describe the role of artificial intelligence and machine learning in medical education and clinical decision support and some future work in augmented reality. It is true that what were once dreams are now reality, but there are certainly more dreams to come.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Wake up Pharma and look into your Big data Yigal Aviv
The vast volumes of medical data collected offers pharma the opportunity to harness the information in big data sets
Unlocking the potential in these data sources can ultimately lead to improved patients outcomes
This presentation describes consideration how to maximize the impact of Big Data.
its methodology, practical challenges and implications.
Presentation of Vishal Gulati (Draper Esprit, Venture Partner; Horizon Discovery Group PLC, Board Director) at the Forum of the BioRegion of Catalonia, organized by Biocat.
Big Data Analytics using in Healthcare Management Systemijtsrd
Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s
The Future Of Health 2014 www.psfk.com/future-of-health / #FutureOfHealth A Foreword PIERS FAWKES Founder & President, PSFK Labs labs.psfk.com Imagine a future where wearable technologies track key areas of your life to provide timely prompts about your health, and the data gathered can be uploaded securely to the cloud. Instead of going into the doctor’s office for a checkup, you would schedule a video consultation to discuss your recent readings. In instances when you need further care, your visits would be coordinated by medical records that flow seamlessly between key members of hospital staff and your care would be supported by relevant information that prepares you for what’s next. Your surgeon would be able to look at your results alongside the wider patient population or seek advice from specialists around the world to determine an optimal treatment plan; the effectiveness of which would determine their compensation. While the realities of the current model of healthcare tell a different story, we’re beginning to see exciting signs of change against daunting challenges. The World Economic Forum estimates that unless current trends reverse, five common ‘lifestyle’ diseases— cancer, diabetes, heart disease, lung disease and mental health problems—will cost the world $47 trillion in treatments and lost wages. Add that figure to a system that could see a shortage of 90,000 doctors in the US alone by the end of the decade, and the picture becomes bleak. Rather than view these as insurmountable obstacles, we choose to see a landscape full of opportunity. Despite a slow regulatory process a host of new mobile and social tools, sensor technologies and devices are being developed for an industry in need of change. These innovations are poised to improve health lifestyle choices and change the way care is delivered. We’re excited to share this patient-centered vision in our latest report.
Interoperable EHR Systems Roundtable Day will provide the unique opportunity for attendees to network with policy makers, EHR service providers, IT specialists, EHR purchasers, and the medical professionals using EHR technology on a daily basis. There is no better way to understand EHR implementation than to put all the players in one room and facilitate an open discussion focused on addressing concerns and obstacles and then discovering applicable solutions.
EHRs, PHRs, EMRs: Making Sense of the Alphabet SoupCHI*Atlanta
CHI*Atlanta's October program tackles health records and the potential of user experience to improve their adoption. Panelists include CDC, Kaiser Permanente, and Greenway Technologies. Hosted at Philips Design to cover public, private, and vendor perspectives.
Andrew Roberts - Mobile Health Apps for Improved Patient Engagement and Educa...itnewsafrica
Andrew Roberts, Chief Information Officer at Clinix Health Group, on Mobile Health Apps for Improved Patient Engagement and Education, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Personal connected health is currently characterized by limited thought leadership, insufficient coordination and collaboration, and a lack of awareness and understanding of the full potential by all stakeholders: public, providers, policymakers, industry and patients. The Personal Connected Health Alliance is defining the the field of personal connected health to inspire market and policy innovation, research and collective action for sustained adoption of personal connected health technology. The vision is better health and well being for all through increased personal responsibilities and connectivity as well as improved care delivery enabled by technology.
Collaborated with the Mayo Clinic's Centre for Innovation on a team project to envision a 2035 future for specialized healthcare providers. Researched trends and drivers from a social, technological, economic, political, environment and values perspective and applied strategic foresight/futures methods to create possible future outcomes. Designed strategies to influence a positive future and mitigate against negative outcomes. The final report was used by the clinic as an innovation input for their multi-year strategic planning activities.
The Future of Medical Education From Dreams to Reality (VR, AR, AI)SeriousGamesAssoc
With three decades of e-learning experience, Dr. Levy will present innovations in technology-enhanced education from the past, present, and into the future. He will highlight some of his medical education inventions and advances including some of the first laser discs, CD-ROMs, online case-based education, 3-D anatomical and procedural animations, robotic-assisted surgery, and virtual reality surgical simulation. He will describe the role of artificial intelligence and machine learning in medical education and clinical decision support and some future work in augmented reality. It is true that what were once dreams are now reality, but there are certainly more dreams to come.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Wake up Pharma and look into your Big data Yigal Aviv
The vast volumes of medical data collected offers pharma the opportunity to harness the information in big data sets
Unlocking the potential in these data sources can ultimately lead to improved patients outcomes
This presentation describes consideration how to maximize the impact of Big Data.
its methodology, practical challenges and implications.
Presentation of Vishal Gulati (Draper Esprit, Venture Partner; Horizon Discovery Group PLC, Board Director) at the Forum of the BioRegion of Catalonia, organized by Biocat.
Big Data Analytics using in Healthcare Management Systemijtsrd
Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s
The Future Of Health 2014 www.psfk.com/future-of-health / #FutureOfHealth A Foreword PIERS FAWKES Founder & President, PSFK Labs labs.psfk.com Imagine a future where wearable technologies track key areas of your life to provide timely prompts about your health, and the data gathered can be uploaded securely to the cloud. Instead of going into the doctor’s office for a checkup, you would schedule a video consultation to discuss your recent readings. In instances when you need further care, your visits would be coordinated by medical records that flow seamlessly between key members of hospital staff and your care would be supported by relevant information that prepares you for what’s next. Your surgeon would be able to look at your results alongside the wider patient population or seek advice from specialists around the world to determine an optimal treatment plan; the effectiveness of which would determine their compensation. While the realities of the current model of healthcare tell a different story, we’re beginning to see exciting signs of change against daunting challenges. The World Economic Forum estimates that unless current trends reverse, five common ‘lifestyle’ diseases— cancer, diabetes, heart disease, lung disease and mental health problems—will cost the world $47 trillion in treatments and lost wages. Add that figure to a system that could see a shortage of 90,000 doctors in the US alone by the end of the decade, and the picture becomes bleak. Rather than view these as insurmountable obstacles, we choose to see a landscape full of opportunity. Despite a slow regulatory process a host of new mobile and social tools, sensor technologies and devices are being developed for an industry in need of change. These innovations are poised to improve health lifestyle choices and change the way care is delivered. We’re excited to share this patient-centered vision in our latest report.
Interoperable EHR Systems Roundtable Day will provide the unique opportunity for attendees to network with policy makers, EHR service providers, IT specialists, EHR purchasers, and the medical professionals using EHR technology on a daily basis. There is no better way to understand EHR implementation than to put all the players in one room and facilitate an open discussion focused on addressing concerns and obstacles and then discovering applicable solutions.
EHRs, PHRs, EMRs: Making Sense of the Alphabet SoupCHI*Atlanta
CHI*Atlanta's October program tackles health records and the potential of user experience to improve their adoption. Panelists include CDC, Kaiser Permanente, and Greenway Technologies. Hosted at Philips Design to cover public, private, and vendor perspectives.
Andrew Roberts - Mobile Health Apps for Improved Patient Engagement and Educa...itnewsafrica
Andrew Roberts, Chief Information Officer at Clinix Health Group, on Mobile Health Apps for Improved Patient Engagement and Education, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Medical System and Artificial Intelligence: How AI assists hospital-dependent...AI Publications
The main objective of this study is to examine how artificial intelligence assists hospital-dependent patients and explore the role of artificial intelligence in the medical system. Hospital-dependent patients have become common in current society due to the elderly with multiple chronic conditions and the COVID-19 infection patient. Thus, it is undeniable that the medical field is lacking healthcare workers. However, in a globalized world, artificial intelligence, the field of science and engineering technology that makes intelligent machines perform given tasks, is chosen to be used as a tool for assisting hospital-dependent patients and collecting databases from the patients. Nevertheless, the paper will cover the use of artificial intelligence in the medical system, hospital-dependent patients as well as provide both positive and negative aspects and the comparison of using artificial intelligence instead of human intelligence. To conclude, we detail how artificial intelligence can take part in the medical system, assist hospital-dependent patients and study further the future of artificial intelligence in the medical system.
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
http://iwma.lnmiit.ac.in/speakers.html
Third International Workshop on Multimedia Applications ( IWMA ), March 02-06, 2021.
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing, image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have explored a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing. In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience.
Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics for healthcare. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health. I will also demonstrate the strong role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
Welcome to the age of cognitive computing: where intelligent machines have
moved from the realms of science fiction to the present day. This groundbreaking
technology is driving advanced discoveries and allowing improved decision-making –
resulting in better patient care
Patient - First Health With Generative AIInsights10
Patient-First Health With Generative AI Learn about the groundbreaking potential of #GenerativeAI in patient engagement, including its three broad categories of use cases. Understand why this innovation is poised to revolutionize patient interaction with their health and the crucial steps stakeholders must take to bring it to fruition. Don't miss this essential read by Insights10 for anyone passionate about the intersection of AI and healthcare! To get a detailed report, contact us at - info@insights10.com, visit - https://bit.ly/42OOgWa
Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
How a Revamped Data Analytics Approach Can Mitigate Healthcare Disparities.pdfTredence Inc
For years now, big data and analytics have contributed significantly to improving patient outcomes and enabling value-based care. According to IDC, approximately 30% of the world’s data is being generated by the healthcare industry.
Learn more: https://www.tredence.com/industries/financial-services
ON EXPLOITING MULTIMODAL INFORMATION FOR MACHINE INTELLIGENCE AND NATURAL IN...Amit Sheth
Keynote: SECOND INTERNATIONAL WORKSHOP IN MULTIMEDIA PRAGMATICSMMPrag 2019, San Jose, California, 28-30 March 2019
http://mipr.sigappfr.org/19/keynote-speakers/
The Holy Grail of machine intelligence is the ability to mimic the human brain. In computing, we have created silos in dealing with each modality (text/language processing, speech processing,image processing, video processing, etc.). However, the human brain’s cognitive and perceptual capability to seamlessly consume (listen and see) and communicate (writing/typing, voice, gesture) multimodal (text, image, video, etc.) information challenges the machine intelligence research. Emerging chatbots for demanding health applications present the requirements for these capabilities. To support the corresponding data analysis and reasoning needs, we have to explore a pedagogical framework consisting of semantic computing, cognitive computing, and perceptual computing (http://bit.ly/w-SCP). In particular, we have been motivated by the brain’s amazing perceptive power that abstracts massive amounts of multimodal data by filtering and processing them into a few concepts (representable by a few bits) to act upon. From the information processing perspective, this requires moving from syntactic and semantic big data processing to actionable information that can be weaved naturally into human activities and experience (http://bit.ly/w-CHE). Exploration of the above research agenda, including powerful use cases, is afforded in a growing number of emerging technologies and their applications - such as chatbots and robotics. In this talk, I will provide these examples and share the early progress we have made towards building health chatbots (http://bit.ly/H-Chatbot) that consume contextually relevant multimodal data and support different forms/modalities of interactions to achieve various alternatives for digital health (http://bit.ly/k-APH). I will also discuss the indispensable role of domain knowledge and personalization using domain and personalized knowledge graphs as part of various reasoning and learning techniques.
POST EACH DISCUSSION SEPARATELYThe way patient data is harvested.docxLacieKlineeb
POST EACH DISCUSSION SEPARATELY
The way patient data is harvested and used is rapidly changing. Patient data itself has become quite complex.
In the future
, patient data will be combined with financial data, product or drug data, socioeconomic factors, social patterns, and social determinants of health. Cognitive behavior and artificial intelligence will be applied to the data to help prevent and depict rather than cure disease.
Evaluate the future of Healthcare information technology.
Include the following aspects in the discussion:
Find two articles related to the future of information systems (IS) in healthcare
Include telehealth, wearable technology, patient portals, and data utilization
Analyze potential benefits from advances
Discuss, from your own perspective, the advantages and disadvantages of having a system where the patient manages their own data
REPLY TO MY CLASSMATE’S DISCUSSION TO THE ABOVE QUESTIONS AND EXPLAIN WHY YOU AGREE. MINIMUM OF 150 WORDS EACH
Classmate’s Discussion 1
The technological advancements that have occurred in the field of healthcare have greatly changed the way people view and interact with the healthcare system. They have also led to the reduction of costs and the increasing efficiency of the system. We expect that the future of healthcare will continue to be influenced by information technology.
Due to the technological advancements that have occurred in the field of healthcare, physicians are now able to spend less time with their patients. This has allowed them to provide more effective and efficient care to their patients. In the future, we can expect that the increasing number of specialists who can delegate their work to other doctors will have a significant impact on the healthcare system. The increasing efficiency of doctors is expected to have a significant impact on the shortage of specialist physicians in the future. This issue could be solved using technology. Hopefully, the use of information technology can help boost the number of specialist physicians (Patric, 2022).
Electronic health records have revolutionized the way healthcare is done. Despite the progress that has been made in terms of keeping and tracking these records, they are still not widely used yet. This means that the kind of growth that was expected from the adoption of these records has not materialized. Although the adoption of electronic health records has been made in various parts of the world, it’s still not widely used in all areas. This means that the ability to keep track of one’s medical history is still very important (Patric, 2022).
The increasing importance of information technology in healthcare has led to the prediction that the cost of healthcare will eventually come down. Various factors such as better accessibility and efficiency will help make healthcare more affordable and more effective.
It’s widely believed that keeping one's health is much cheaper and easier than treating a.
Data Analytics for Population Health Management Strategiesijtsrd
Data analytics plays a pivotal role in population health management, offering strategies to enhance healthcare delivery and outcomes. This review article delves into the multifaceted world of data analytics in the context of population health management. It explores the utilization of health data for risk stratification, predictive modeling, and interventions tailored to the needs of distinct population groups. The article discusses the integration of electronic health records, wearables, and IoT devices to gather comprehensive patient data. Analytical methods, including machine learning and data mining, are examined for their capacity to extract insights from large datasets. The importance of data privacy, security, and ethical considerations in population health management is also addressed. In conclusion, this article underscores the significance of data analytics in optimizing population health management strategies and improving healthcare outcomes. Ravula Sruthi Yadav | Dipiksha Solanki "Data Analytics for Population Health Management: Strategies" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60104.pdf Paper Url: https://www.ijtsrd.com/pharmacy/pharmacology-/60104/data-analytics-for-population-health-management-strategies/ravula-sruthi-yadav
www.panorama.com
Panorama Necto uncovers the hidden insights in your data and presents them in beautiful dashboards powered with KPI Alerts, which is managed by a the most secure, centralized & state of the art BI solution.
Pharma Social Media Listening: Unlocking Hidden Insights | WhitepaperRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
Unlocking Hidden Insights for Pharma with Social Media ListeningRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
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]
Similar to Knowledge-enhanced Learning @ Kno.e.sis (20)
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
1. A quick review of Kno.e.sis’ research with an emphasis on
knowledge-enhanced learning
with impact on health and social good
Why? What? How?
Feb 2019 Snapshot
2. Dynamic Knowledge Graph - Temporal Information Retrieval
Search for event-relevant information on the web is prone to incorrect or incomplete or
stale information. Inferring temporal information associated with events and related
assertions can significantly improve the quality of Q/A on the Web. Hence, there is a
need to identify and maintain temporally changing information to analyze complex
temporal dynamics and interactions of entities during a series of evolving events.
WHY ?
Swati Padhee
swati@knoesis.org
WikiPedia
U S Senator U S Presidential Candidate
Semi-Structured KG
source
Unstructured real-time
Knowledge source
Kamala Harris
FIFA World Cup
…. ….
Followed by
4 years
Champion ?
Host Country
France
Russia ?
After
4 years
Politics
Sports
Complex Temporal QA/Conversations:
Who is the Champion of FIFA World Cup
in Jan 2019?
Temporal Knowledge Validity: Who will be
the President of United States in May
2021?
APPLICATIONS
We rely on reasoning over unstructured and structured Knowledge
Graphs (KGs). However, most traditional KGs capture static multi-
relational data. Effectively capturing the temporal dependencies across
knowledge sources can help improve the understanding on complex
temporal dynamics of entities and their evolution over time.
WHAT ?
We define two problems:
(1)Automatically extracting and predicting patterns for a class of recurrent
periodic events (e.g. FIFA World Cup).
(2)Inferring temporal knowledge for non-periodic events (e.g. disasters)
from real-time multimodal data to create evolving Dynamic Knowledge
Graph.
We rely on combining text mining approaches with machine learning
using knowledge from: (1) hierarchical and non-hierarchical relationships
in KGs, (2) unstructured textual event-specific information, and
(3) semi-structured collaborative KGs.
HOW ?
3. Personalized Healthcare Knowledge Graph (PHKG)
➢ AI will provide additional patient support (e.g., for
continuous/remote monitoring/consult).
➢ Existing health applications enable data
visualization, humans must interpret such data.
➢ Humans are overwhelmed by patient generated
health data and voluminous search results.
➢ Continuously convert diverse patient’s health
related data into health related concepts
(abstractions) as PHKGs.
➢ Interprets health data to build smarter health
applications (e.g., recommendations) and
conversational systems (e.g., chatbots).
➢ Encoding domain expertise and patient data to be
processable by machines using Semantic Web
technologies (RDF, RDFS, OWL, SPARQL).
➢ Use knowledge-graph enhanced learning to
interpret health data within the patient’s health
context and PKHG (derive abstractions through
personalized and contextualized data processing).
WHY
WHAT
HOW
Amelie Gyrard
amelie@knoesis.org
kHealth project
Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth.
Personalized Health Knowledge Graph. Contextualized Knowledge Graph (CKG) Workshop, International Semantic Web Conference (ISWC) 2018
4. WHAT ?
Modeling the automatic medical codes assignment as an Extreme
Multi-label Classification (XMC) problem and provide auxiliary
supervision through external knowledge to achieve precise and reliable
code assignment.
Medical Codes Prediction
by infusing knowledge with Deep Learning
WHY ?
Medical codes assignment on clinical notes is an important step in
record keeping, structuring in healthcare systems and insurance
claiming. Due to i) the challenges in understanding medical language,
ii) the extensive use of medical jargons, drugs and procedure names in
clinical notes, and iii) the huge label space, manual annotation has
become a labor and time intensive, error-prone and a difficult task.
HOW ?
We use the most-widely cited MIMIC-III clinical notes dataset with
~59k hospital stays of ~48k patients. Our objective is to use the state-
of-the-art Deep Learning frameworks coupled with external medical
knowledge such as UMLS, SNOMED to predict all the relevant ICD codes
from a huge 9k label space.
Ruwan Wickramarachchi
ruwan@knoesis.org
5. SMART Chatbots for Enhanced Health
Using Multisensory Sensing & Semantic-Cognitive-Perceptual
Computing for Augmented Personalized Health
Understanding and managing health is both complex and costly and we
have relied on clinicians for most health-related decision making throughout
the last few decades of modern medicine. Additionally, the challenge to
capture different modalities and making sense of health data requires
advances in contemporary knowledge-based processing approaches.
Smart chatbots provide superior way to collect data and interact with
intelligent/AI based systems to empower patients to better manage their
health. Augmented Personalized Health provides increasingly sophisticated
and comprehensive options for health management from self-monitoring,
self-appraisal, self-management, intervention, to predictions, without
overburdening clinicians.
AI-based intelligent (semantic/cognitive/perceptual) computing converts
diverse and continuously collected health data (esp. PGHD, IoT/sensors,
environmental, clinical) and medical knowledge into highly personalized and
contextual abstractions that enable deeper insights into health conditions
and/or timely actions leading to improved health outcomes. Chatbots
utilizing conversational AI (ie. reinforcement learning) provide superior
human-computer interface.
WHAT
WHY
HOW
http://bit.ly/Humanlike-Chatbots http://bit.ly/Smart-Chatbots
Joey Yip
joey@knoesis.org
6. Causal Inference Analysis in Pediatric Asthma Patients
➢ Each pediatric asthma patient is DIFFERENT and thus, understanding of their
personalized symptoms and treatment is needed
➢ LIMITED DATA due to episodic visits
➢ TIME CONSTRAINT during clinical visits, significant information seeking time is
required on every clinical visit
➢ Comprehending clinical notes which contains only text is difficult
Continuous monitoring of pediatric asthma patient’s health signals (such as sleep
pattern, daily activity, symptoms, potential environmental triggers and medication
compliance) using sensors to get personalized information which can be incorporated by
the clinician in the care protocol for timely intervention, better management of the
disease and adherence of the patient towards their care protocol.
Development of a personalized causal inference analysis using
probabilistic graphical models for abstracting actionable information
from the vast amount of multimodal health signals to understand
relationship between asthma symptoms, potential triggers,
medication compliance, adherence towards the care protocol and
suggest precautionary measures to avoid future exposure to triggers
leading to worsening of the disease control. Utkarshani Jaimini
utkarshani@knoesis.org
WHY
WHAT
HOW
7. Fusing Visual, Textual and Connectivity
Clues for Studying Mental Health
Clinical Depression is one the most common mental illness that affects
350m people and has $42 billion annua; cost. On a third of those who
suffer from depression receive treatment. Traditional survey-based
methods via phone or online questionnaires suffer from under-
representation, sampling bias, and temporal gaps.
Social media platforms are a valuable resource for learning about users’
feelings, and behaviors that reflect their mental health as they are
experiencing the ups and downs. Emotional state from visual/textual content,
users’ desire to socialize and connect with others can be a proxy for our online
persona. Gleaning social signals by modeling user-generated content in social
media, we can emulate traditional observational cohort studies conducted
through online questionnaires.
1) How textual and visual content in social media can be harnessed to reliably
capture clinical depression symptoms of a user over time?
2) How does the choice of profile picture show any psychological traits of
depressed online persona? Are they reliable enough to represent the
demographic information such as age and gender?
3) How do we generalize them to infer population-level attitudes towards care?
4) How well does geographical information gleaned from depressed
individuals over social media can serve as the basis for effective community-
level management of depression by studying patterns of access, utilization,
and location of mental health services?
WHAT
HOW
Thinking about
hanging myself ... I
just don't want to
wake up tomorrow
morning.
I feel like a
failure
Amir Yazdavar
yazdavar@gmail.com
WHY
8. 81. Gaur, Manas, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "Let Me Tell You About Your
Mental Health!: Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM CIKM 2018.
Classification of Reddit Content to DSM-5 for Web-based Intervention
● Precise diagnosis of mental health condition can enable effective
remedial actions.
● However, there is no such framework that associate a user's condition to
a diagnostic category that assist a Mental Health Professional in giving
precise advice and improve quality of care.
● The use of historical information and personalization of care can
accrue economic benefits and improved quality of care by reducing
number of visits to the mental health clinic.
Patient
ClinicianEMR
Insight
DSM-5 & Drug Abuse Ontology
Improved
Healthcare
Provide clinicians insights on their patients
● To address concerns on Mental health, we developed a novel framework that complements the existing healthcare
system.
● We leverage 11 years (2005-2016) of 8 Million conversations from 600K users on Reddit and DSM -5 Knowledge
hierarchy together with SNOMED-CT, ICD-10, and UMLS to develop an AI solution that matches user's content to
suitable Mental Health specialization.
Why
What
9. Outcomes & Insights
9
● In order to operationalize the goal, we propose an
approach to map the content to more rigorously
defined DSM-5 categories using Coherence-LDA and
Semantic NLP.
● For reducing the false alarm rate, we developed a novel
Zero-Shot Learning inspired Semantic Encoding and
Decoding Optimization (SEDO) approach that generate
semantic word vectors using a medical knowledge
hierarchy.
● SEDO improves the classification by decreasing false
alarm rate by 92% without changing the classifier
● The result of our framework was evaluated by
practicing clinical psychiatrist, reaching an
agreement of 84%.
● As a broader impact:
○ Our approach is unsupervised
○ Relies on Domain-Specific Knowledge Hierarchy
to generate Semantic Word Vectors.
How
Manas Gaur
manas@knoesis.org
10. Reason and Effect relationship extraction
between Cannabis use and Depression
Why: Upon the increasing efforts on legalization of cannabis use in US,
the relationship of cannabis use with depression remains ambiguous
as to whether cannabis use is a reason for depression or it is a
subsequent effect. Automatic identification of its relationship with
depression in big social data would enable public health analysts to
gain insights on these relationships and their prevalence in public
health.
What: Automatically extract the relationships of reason and effect
between cannabis and depression building a deep learning framework
enhanced with domain-specific knowledge.
How: Employ Drug-Abuse Ontology (DAO), to enhance representations of
the entities in tweets. We propose a top-down technique of using
DAO to semantically augment the deep learning framework (CNN) in
the form of entity position-aware attention.
Usha Lokala
usha@knoesis.org
11. User Modeling in Marijuana-related Communications
11
● Public opinion on marijuana-related legalization efforts in U.S. needs to be
assessed based on only personal views of individuals who actually vote in the
elections, since media and retail accounts on social media represent institutional
views that influence personal views.
● Hence, proper separation of Personal accounts from Media and Retail accounts
in Marijuana communications is necessary for accurate measurement of public
opinion and the influence of media and retail accounts over personal accounts
through social media.
WHY
● We model users based on characteristics in their profile (people), content and
network interactions, which we call as views, by incorporating multimodal
data through Compositional Multiview Embeddings for coherent and unified
representations.
● These user types show distinct characteristics in volume, network
interactions, the use of domain-specific concepts and different modalities in
content. As each of these different facets of information contributes to the
meaning, their contribution to the representation of a user will differ as well.
WHA
T
Ugur Kursuncu
ugur@knoesis.org
12. User Modeling in Marijuana-related Communications
12
● Generate coherent and unified representations of users
through Compositional Multiview Embeddings based on the
views of People, Content, Network (PCN).
● Domain-specific embedding models are developed for each
view based on user descriptions (people), tweets (content) and
network interactions (network) from a corpus of ~1M users.
● Multimodal data is incorporated in each view translating the
data in each form (Text, Image, Emoji, Network Interactions) into
a uniform representation through state-of-the-art techniques
such as EmojiNet.
● For each view, multimodal elements are as follows:
● People: user description, emoji, profile pictures.
● Content: text, emoji
● Network: interactions with other users: retweets and
mentions.
HOW
🏈😉
😸🍔
🎈🎨
Multimodal
Data
Incorporation
Composition
of Multiview
Embeddings
Generate
Representation
of users based
on PCN
Personal Retail Media
13. eDarkTrends: Trend Analysis of
Opioid on Cryptomarkets
Why As opioid overdose death rates has been climbing in US in recent
years, Dark-Web has become an essential venue where individuals can find
illicit synthetic opioid products. Hence, monitoring of the products on these
platforms will provide invaluable insights to the analysts on its prevalence
in the society with respect to the overdose incidents.
What Developed a semi-automated system, eDarkTrends, to collect and
process data about illicit synthetic opioids supplied on cryptomarkets. We
perform trend analysis for US through information extraction: price, purity,
dosage, quantity, and drug combinations. We also identify new illicit
synthetic opioid substances and product forms, soon after they appear on
cryptomarkets.
How Drug Abuse Ontology (DAO) is utilized for information extraction, as the
DAO contains informal terms being used on cryptomarkets. Domain-
specific named-entity recognition is performed. Machine learning models
coupled with the DAO, is employed to identify new entities (e.g. opioids) with
validation by domain experts. We perform correlation analysis for trend
estimation.
Usha Lokala
usha@knoesis.org
14. 14Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kurşuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randon S. Welton and Jyotishman Pathak.
"Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention". The Web Conference 2019. San Francisco, California.
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
Knoesis wiki for Modeling Social Behavior for Healthcare
Utilization in Depression
● Mental health illness such as depression is a significant
risk factor for suicidal ideation and behaviors, including
suicide attempts.
● A quantitative assessment of suicide risk for informed
timely clinical decision-making and early intervention.
● Prior research involving surveys and questionnaires (e.g.,
PHQ-9) for suicide risk prediction failed to provide a
quantitative assessment of risk that informed timely
clinical decision-making for intervention.
● Our interdisciplinary study concerns the use of Reddit as
an unobtrusive data source for gleaning information
about suicidal tendencies afflicting depressed users
using C-SSRS.
*C-SSRS Columbia Suicide Severity Rating Scale
Progression of users through severity levels of suicide
risk
Why
15. Outcomes & Insights
15
● Initially, the framework performs medical entity
normalization using suicide risk severity lexicon
to abstract the content into clinically
understandable language.
● Through convolution operation in the neural
network, the model identifies discriminative
features useful to perform ordinal classification
into one of five labels.
● The deep learning framework was to able to
reduce the probability of misclassification by
12.5 %.
● Importantly, our approach distinguishes people
who are supportive, from those who show
different severity of suicide.
How
● Develop a knowledge-aware deep
learning framework with perceived risk
measure for predicting the severity of
suicide risk individuals.
● Build domain expert (Mental Health
Professional)-curated suicide risk
severity lexicon with following suicide risk
severity levels as classes: supportive,
indicator, ideation, behavior, attempt.
● Demonstrate the efficiency of the
framework on a gold standard dataset of
500 Redditors obtained from practicing
clinical psychiatrists.
What
Manas Gaur
manas@knoesis.org
16. Assessing Severity of Health States
in Social Media Posts
Around 80% of Internet users in the US explore health-related
topics in online health communities [Pew]: 63% look for
information about a specific medical problem, and 47% look for
the medical treatment or procedure. Understanding the degree of
severity on health forums can (i) assist the human moderators for
providing timely response and interventions, and (ii) support
pharmacovigilance studies for identifying adverse drug reactions.
We develop an advanced understanding of the severity of patients’
health state from social media posts based on medical condition
(e.g., exist, recover, deteriorate) and the outcome of treatment or
medication (e.g., effective, ineffective, and serious adverse effect).
Our multifaceted framework leverages several aspects of Natural
Language Understanding (NLU) for making an inference.
A deep neural network based multifaceted framework utilizing the
textual content as well as various NLU features (e.g., sentiment,
emotions, etc.) assesses a user's health on certain aspects
(medical condition & medication) for enabling timely intervention.
WHAT
WHY
HOW
Joy Prakash Sain
joy@knoesis.org
17. Why?
Obesity is on the rise worldwide. Focus on reducing excess calorie consumption and
making an informed decision about food choices and physical activity can help
attain a healthier weight and reduce the risk of chronic illness (cf: The Dietary
Guidelines of Americans).
What?
Monitoring individual's diet and cumulative calorie intake through food images and
recommending meals can help them in making informed decisions about their
meals. Also, tracking and assessing their food patterns and weight trends can help
them maintain healthier weight in the longer run.
How?
Built a system that is trained to recognize food images collected from open sources
such as Instagram, Google images, Pinterest, Getty image, etc. Once recognized,
volume can be estimated based on user input (automatically, in future) and nutrition
information can be obtained using comprehensive knowledge bases. AI techniques
support meal recommendations specific to user preferences and context.
Current Research Problem: Currently working on infusing knowledge to enhance image
classification. The existing systems face limitations as the classification is done using low
level features such as pixels, which leads to overlap among classes. That overlap can be
clarified with external knowledge.
Nutrition Management Information System
Nutrition Information
Management System
Revathy Venkataramanan
revathy@knoesis.org
18. Translational Research - Detecting Sample Mislabeling
(winning precisionFDA challenge!)
The use of high throughput molecular profiling methods is becoming increasingly
common in genetic studies to understanding the disease and enhancing our ability to
achieve the promise of precision medicine. The effectiveness of the methods depends
critically on the accurate labeling of the samples and could be seriously weakened by
sample mislabeling. However, the issue of sample mislabeling is obscure as it occurs
at data entry level, which the data will be considered as the ground truth and used for
downstream analysis.
Why
Multi-omics data is a multimodal data consisting of genomic, epigenomic,
transcriptomic and proteomic. Different modality of the data correlate with each other
and are highly parallel in nature. These data contain variation among different groups
of patients with different clinical attributes. Here, We exploit the coherency of
information inferred from different modality of the data to inform the potential
mislabeled samples.
What
We developed a computational algorithm to model the relationship between clinical
attributes, protein profiles and mRNA profiles. The model is applied to detect
mislabeled samples and correct the label. Accurate detection of mislabeling enhances
assurance that patients are getting the right analysis and prevent irreversible
consequences of giving wrong treatment to patients.
How
Clinical
Attributes
Transcriptomic Proteomics
Healthy
Healthy
Patient
Patient
SoonJye Kho
soonjye@knoesis.org
www.soonjye.com
19. Context-Aware Harassment Detection on Social Media
As social media permeates our daily life, there has been a sharp rise in the
use of social media to humiliate, bully, and threaten others, leading to
consequences ranging from emotional distress, depression to suicide.
Identifying such instances of harassment is challenging due to the
nuanced nature of human communication.
WHY
Analyze social media data to understand and identify the phenomenon of
online harassment.
WHAT
Employ syntactic, semantic and contextual cues (unfettering harassment
from keyword based approaches) with machine learning and deep learning
techniques on twitter data to introduce methods that would help to better
identify online harassment instances.
HOW
Thilini Wijesiriwardene
thilini@knoesis.org
20. Enhancing crowd wisdom using diversity
measures computed from social media
The predictive analytics market is expected to grow
from USD 4.56 Billion in 2017 to USD 12.41 Billion by
2022. Crowd selection is the most fundamental task
in the prediction market.
Diverse crowd selection strategies that can select
unbiased groups of individuals using process data
without relying on performance or outcome data. The
proposed strategies assembled crowds that could
accurately predict geopolitical and sports event
outcomes.
WHAT
WHY
HOW
Shreyansh Bhatt
shreyansh@knoesis.org
We propose the use of social media data to infer
diversity and select diverse (unbiased) group of
individuals. The proposed data-driven diversity
measure characterizes a user with word2vec and
selects a diverse crowd using clustering. An
enhanced diversity measure using domain-specific
knowledge graph for diverse crowd selection.
HOW
21. In 2017 alone the USA suffered from more than 16 weather-
related natural disasters which resulted in 452 fatalities and
a record $306 billion in economic cost. This highlights the dire
need for better capabilities to manage —plan, prepare and
respond— when such disasters strike.
Multimodal Data Aggregation and Integration
for Disaster Coordination and Response
Our multi-dimensional cross-modal aggregation and
inference methods integrate imagery, sensory, and textual
data. We preserve low-level details to provide situational
awareness for individuals, first responders, and humanitarian
organizations and the kind of available/required help for
individuals' needs including flooded areas around them.
We integrate the output of our semantic, syntactic and
pragmatic information extraction techniques with other
hazard models such as flood mapping. To achieve that, we
cross reference and integrate different knowledge sources (e.g.,
ontologies and gazetteers, resources) with streams of texts,
images and sensors (drone, satellite) data in real-time.
WHAT
WHY
HOW
54321
Contact: Hussein Al-Olimat (hussein@knoesis.org) http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support
22. More projects and specifics at http://knoesis.org (library, projects, …)
Slides: https://www.slideshare.net/knoesis
General: Kno.e.sis on FB: https://facebook.com/kno.e.sis
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This subset of Kno.e.sis research is primarily
directed by these faculty members
Dr. Amit P. Sheth Dr. Krishnaprasad Thirunarayan Dr. Valerie Shalin
Editor's Notes
As usual---I'm wondering what the technical challenge is. A lack of precise (formal)? And why don't we have that already? Or do, we, but it is spread out across resources? If it is spread out, what are the integration challenges?
Societal Challenges (SC) and Technical Challenges (TC) for building a PHKG are investigated as follows:
SC1: What recommendations can be suggested by health applications to assist patients?
SC2: How personalized health coach applications can help clinicians?
SC3: Web sites such as airnow.gov and pollen.com provide visualization of environmental factor’s datasets according to their quality (e.g., low or high) which is mainly used by humans to understand the current environmental condition. How can machines automatically interpret and get this information? We need to provide the range (e.g., between X and Y then it is considered HIGH) to the machine to understand the data either with rule-based reasoning (used in this paper) or machine learning techniques (considered as future work).
SC4: How to diagnose asthma patients?
SC5: What environmental conditions (e.g., pollen level) impact a patient and trigger asthma symptoms (e.g., cough)?
TC1: How to build a PHKG? How are Google Health KG or IBM Watson KG built? Information provided in tutorials such as [6] do not provide concrete steps to construct a KG.
TC2: How to deduce meaningful information from kHealth Asthma datasets (EHRs, IoT datasets)?:
TC3: How to maintain data privacy and security of patient data? We are aware of those concerns, data is anonymized, but we do not detail this challenge in this paper.
What (Old)- Understanding the asthma triggers, their cause and individual treatment effect for pediatric asthma patients using continuous monitoring of their health signals.
How (Old)- 1) Causal inference analysis from multimodal data using Probabilistic Graphical Models for pediatric asthma patient. 2) Understanding personalized symptoms, asthma triggers and treatment, requires a causal inference analysis of multimodal data using Probabilistic Graphical Models for pediatric asthma patients.
Help clinician in detailed analysis.