Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
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.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
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
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
The impact of cloud and big data on healthcare sector (1)Mindfire LLC
A lot of data is produced on a routine basis by hospitals, laboratories, retail, and non-retail medical operations and promotional activities. But most of it gets wasted because respective persons are not able to figure out what to do with that data. This is where Cloud-based Big Data comes into the picture. The big data analytics tools and repositories remove the hard thinking and generate reliable and calculative insights out of huge volumes of data within a matter of seconds. This means in the future we will need more doctors who are trained to work with big data .
Our Journey to Release a Patient-Centric AI App to Reduce Public Health CostsDatabricks
Health costs are exploding year by year. Thanks to Artificial Intelligence it is possible to address patient needs in a cost-efficient manner.
In the case we will present, we will demonstrate how as part of a telemedicine service we implemented a solution allowing to reduce triage cost of patients by leveraging AI. The app we developed not only allowed to reduce cost but is significantly improving the patient experience.
The health emergency underway worldwide has highlighted the need to strengthen the surveillance and care of the sick at home, to avoid hospital overcrowding that we have seen in recent months inevitably compromise the management of the emergency itself. X-RAIS is an AI tool, which as a third eye supports radiologists during the reporting phase of radiological images. Within this context, we extended X-RAIS capabilities with ALFABETO (ALl FAster BEtter TOgether).
Patient centricity and digital solutionsAhmed Graouch
Beyond product offerings, it also positions Medtech companies to help hospitals and health systems transition to the future of health through services.
The term “digital twin” refers to the digital version of a physical device or process. By bridging the physical and the virtual worlds, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical device or process. Digital twins are emerging as virtual test beds for
possible solutions before they implement physical devices. These computer-based models are fed individual and population data and mimic the electrical and physical properties of an object.
Medical device companies are using this technology to simulate how their devices are being used in the
clinical setting.
In our view of the future of health, radically interoperable data is likely to play a huge role in transforming health care. Data from medical technologies such as wearables, remote monitors, and
sensors will be standardized, stored, updated, and aggregated with other sources of information such as social media platforms, retailers, and electronic health records.
The combined data will create a complete personal profile that physicians and health systems can use to help ensure that
I deliver health services in an appropriate fashion.
apidays LIVE India - The digitisation of healthcare by Dr S.S. Lal, Global Fo...apidays
apidays LIVE India 2021 - Connecting 1.3 billion digital innovators
May 20, 2021
The digitisation of healthcare
Dr S.S. Lal, President of Global Foundation for Health and Hygiene
Day 2 Big Data panel at the NIH BD2K All Hands 2016 meetingWarren Kibbe
Big data in oncology and implications for open data, open science, rapid innovation, data reuse, reproducibility and data sharing. Cancer Moonshot, Precisions Medicine Initiative (PMI), the Genomic Data Commons, NCI Cloud Pilots, NCI-DOE Pilots, and the Cancer Research Data Ecosystem.
Bioinformatics in the Clinical Pipeline: Contribution in Genomic Medicineiosrjce
In this review report we like to focus on the new challenges in methodology of modern biology be
used in medical science. Today human health is a primary issue to cure disease, undoubtedly the answer to this
is bioinformatics or (In-silco) tools has change the concept of treating patients to understand the need of
genomic medicine in use. Those with new modes of action in clinical treatment, is a major health concern in
medical science. On global prospective scientific role in constructing new ideas to remediate health care to
treat disease exciting in nature is challenging task. So awareness needs to accelerate store clinical datasets for
scientific represents to design genomic drugs. This new outline will drive the medical to discover public data
and create a cognitive approach to use technology cheaper at cost effective mode.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
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.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
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
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
The impact of cloud and big data on healthcare sector (1)Mindfire LLC
A lot of data is produced on a routine basis by hospitals, laboratories, retail, and non-retail medical operations and promotional activities. But most of it gets wasted because respective persons are not able to figure out what to do with that data. This is where Cloud-based Big Data comes into the picture. The big data analytics tools and repositories remove the hard thinking and generate reliable and calculative insights out of huge volumes of data within a matter of seconds. This means in the future we will need more doctors who are trained to work with big data .
Our Journey to Release a Patient-Centric AI App to Reduce Public Health CostsDatabricks
Health costs are exploding year by year. Thanks to Artificial Intelligence it is possible to address patient needs in a cost-efficient manner.
In the case we will present, we will demonstrate how as part of a telemedicine service we implemented a solution allowing to reduce triage cost of patients by leveraging AI. The app we developed not only allowed to reduce cost but is significantly improving the patient experience.
The health emergency underway worldwide has highlighted the need to strengthen the surveillance and care of the sick at home, to avoid hospital overcrowding that we have seen in recent months inevitably compromise the management of the emergency itself. X-RAIS is an AI tool, which as a third eye supports radiologists during the reporting phase of radiological images. Within this context, we extended X-RAIS capabilities with ALFABETO (ALl FAster BEtter TOgether).
Patient centricity and digital solutionsAhmed Graouch
Beyond product offerings, it also positions Medtech companies to help hospitals and health systems transition to the future of health through services.
The term “digital twin” refers to the digital version of a physical device or process. By bridging the physical and the virtual worlds, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical device or process. Digital twins are emerging as virtual test beds for
possible solutions before they implement physical devices. These computer-based models are fed individual and population data and mimic the electrical and physical properties of an object.
Medical device companies are using this technology to simulate how their devices are being used in the
clinical setting.
In our view of the future of health, radically interoperable data is likely to play a huge role in transforming health care. Data from medical technologies such as wearables, remote monitors, and
sensors will be standardized, stored, updated, and aggregated with other sources of information such as social media platforms, retailers, and electronic health records.
The combined data will create a complete personal profile that physicians and health systems can use to help ensure that
I deliver health services in an appropriate fashion.
apidays LIVE India - The digitisation of healthcare by Dr S.S. Lal, Global Fo...apidays
apidays LIVE India 2021 - Connecting 1.3 billion digital innovators
May 20, 2021
The digitisation of healthcare
Dr S.S. Lal, President of Global Foundation for Health and Hygiene
Day 2 Big Data panel at the NIH BD2K All Hands 2016 meetingWarren Kibbe
Big data in oncology and implications for open data, open science, rapid innovation, data reuse, reproducibility and data sharing. Cancer Moonshot, Precisions Medicine Initiative (PMI), the Genomic Data Commons, NCI Cloud Pilots, NCI-DOE Pilots, and the Cancer Research Data Ecosystem.
Bioinformatics in the Clinical Pipeline: Contribution in Genomic Medicineiosrjce
In this review report we like to focus on the new challenges in methodology of modern biology be
used in medical science. Today human health is a primary issue to cure disease, undoubtedly the answer to this
is bioinformatics or (In-silco) tools has change the concept of treating patients to understand the need of
genomic medicine in use. Those with new modes of action in clinical treatment, is a major health concern in
medical science. On global prospective scientific role in constructing new ideas to remediate health care to
treat disease exciting in nature is challenging task. So awareness needs to accelerate store clinical datasets for
scientific represents to design genomic drugs. This new outline will drive the medical to discover public data
and create a cognitive approach to use technology cheaper at cost effective mode.
Cancer Moonshot, Data sharing and the Genomic Data CommonsWarren Kibbe
Gave the inaugural Informatics Grand Rounds at City of Hope on September 8th. NIH Commons, Genomic Data Commons, NCI Cloud Pilots, Cancer Moonshot and rationale for changing incentives around data sharing all discussed.
Quantifying the content of biomedical semantic resources as a core for drug d...Syed Muhammad Ali Hasnain
The biomedical research community is providing large-scale data sources to enable knowledge discovery from the data alone, or from novel scientific experiments in combination with the existing knowledge.
Increasingly semantic Web technologies are being developed and used including ontologies, triple stores and combinations thereof.
The amount of data is constantly increasing as well as the complexity of data.
Since the data sources are publicly available, the amount of content can be derived giving an overview on the accessible content but also on the state of the data representation in comparison to the existing content.
For a better understanding of the existing data resources, i.e.\ judgments on the distribution of data triples across concepts, data types and primary providers, we have performed a comprehensive analysis which delivers an overview on the accessible content for semantic Web solutions.
It can be derived that the information related to genes, proteins and chemical entities form the center, whereas the content related to diseases and pathways forms a smaller portion.
Further data relates to dietary content and specific questions such as cancer prevention and toxicological effects of drugs.
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.
The state of the art in behavioral machine learning for healthcareAfrica Perianez
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease.
But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model.
In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.
Big Data Europe SC6 WS #3: PILOT SC6: CITIZEN BUDGET ON MUNICIPAL LEVEL, Mart...BigData_Europe
Presentation at the Big Data Europe SC6 workshop #3 on 11.9.2017 in Amsterdam co-located with SEMANTiCS2017 conference: BDE PIlot Societal Challenge 6: CITIZEN BUDGET ON MUNICIPAL LEVEL by Martin Kaltenboeck (Semantic Web Company, SWC).
Big Data Europe SC6 WS #3: Big Data Europe Platform: Apps, challenges, goals ...BigData_Europe
Talk at the Big Data Europe SC6 workshop number 3 taking place on 11.9.2017 in Amsterdam co-located with SEMANTiCS2017 conference: The Big Data Europe Platform: Apps, challenges, goals by Aad Versteden, TenForce.
Big Data Europe SC6 WS 3: Where we are and are going for Big Data in OpenScie...BigData_Europe
Where we are and are going for Big Data in OpenScience
Keynote talk at the Big Data Europe SC6 Workshop on 11.9.2017 in Amsterdam co-located with SEMANTiCS2017: The perspective of European official statistics by Fernando Reis, Task-Force Big Data, European Commission (Eurostat).
Big Data Europe SC6 WS 3: Ron Dekker, Director CESSDA European Open Science A...BigData_Europe
Slides for keynote talk at the Big Data Europe workshop nr 3 on 11.9.2017 in Amsterdam co-located with SEMANTiCS2017 conference by Ron Dekker, Director CESSDA: European Open Science Agenda: where we are and where we are going?
Big Data Europe: SC6 Workshop 3: The European Research Data Landscape: Opport...BigData_Europe
Slides of the keynote at the 3rd Big Data Europe SC6 Workshop co-located at SEMANTiCS2018 in Amsterdam (NL) on: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS, Chair, Science Europe W.G. on Research Data. Chair, CESSDA ERIC General Assembly
BDE SC3.3 Workshop - Options for Wind Farm performance assessment and Power f...BigData_Europe
Options for Wind Farm performance assessment and Power forecasting (Mr. A. Kyritsis, ALTSOL/TERNA) at the BigDataEurope Workshop, Amsterdam, Novermber 2017.
Big Data Europe: Workshop 3 SC6 Social Science: THE IMPORTANCE OF METADATA & ...BigData_Europe
Big Data Europe: Workshop 3 SC6 Social Science - 11.09.2017 in Amsterdam, co-located with SEMANTiCS2017 titled: THE IMPORTANCE OF METADATA & BIG DATA IN OPEN SCIENCE. Slides by Ivana Versic (Cessda) and Martin Kaltenböck (SWC)
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Big Data Analytics in the Health Domain
1. Integration and analysis of heterogeneous big data for precision
medicine and suggested treatments for different types of patients.
SC1-PM-18-2016: Big Data supporting Public Health policies
Big Data Analytics
Maria-Esther Vidal
Leibniz Information Centre For
Science and Technology
University Library (TIB), Germany
06.11.2017
BigDataEurope Workshop on Big Data in Climate Action,
Environment, Resource Efficiency and Raw Materials
1
http://project-iasis.eu
@Project_IASIS
2. Big Data Analytics and Drug Side Effects
2
Big Data Analytics study has found an association
between the use of proton-pump inhibitors and the
likelihood of incurring a heart attack
[Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.]
Heart Attack
Electronic Health Records (EHRs) of nearly 3 million
people and trillions of pieces of medical data
Big Data
3. Big Data Analytics and Disease Predisposition
Researchers analyzed more than 7,700 brain
images from 1,171 people in various stages of
Alzheimer's progression using a variety of
techniques including magnetic resonance imaging
(MRI) and positron emission tomography (PET).
Alzheimer’s Disease Evolution
Big Data Analytics study has found that changes in
blood flow are the earliest known warning sign of
Alzheimer's.
Big Data
https://www.sciencedaily.com/releases/2016/07/160712130229.htm
3
4. Big Data: Medical Image
Large volumes of data produced by Imaging Techniques
Volume of medical images is growing exponentially
Annotated data or structured methods to annotate
medical images is challenging
Big Data Analytical Methods allow for the interpretability
of depicted contents
Scalable Methods for collecting, compressing, sharing,
and anonymizing medical data
Medical Image Processing from Big Data Point of View
4
5. Electronic Health Records (EHR) from Big Data Point of View
Big Data: Clinical Data
Large volumes of Clinical Data that needs to be
stored, retrieved, and aggregated
Scalable Methods for collecting, compressing,
sharing, and anonymizing medical data
Scalable Methods for signal processing and for
developing Big Data based clinical decision support
systems (CDSSs)
5
6. Genomics from Big Data Point of View
Big Data: Genomics Data
Human Genome consists of 30,000 to 35,000 genes
Scalable Methods for pathway analysis and for the discover
associations between observed gene expression changes and
predicted functional effects
Scalable Methods for Reconstruction of Metabolic and
Regulatory Networks
6
10. Agenda
1. iASiS: Big Data to Support Precision Medicine and Public
Health Policy
2. The iASiS Architecture
3. Big Data Management and Analytics in iASiS
10
11. Drug Ineffectiveness
11
[Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffrey Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, pages 201-204.]
13. Drug Side Effects
13
https://www.topcanadianpharmacy.org/wp-content/uploads/2016/09/Plavix-package.jpg
Drug designed to prevent blood clots
The second-best-selling drug in the world
Different impact on protecting stent patients
from thrombosis depending on patient genetic
variance within CYP2C19
CYP2C19 encodes an enzyme that converts the
drug from an inactive to an active state.
[Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.]
The right drug and dosage are
selected based on a patient
genome
14. Precision Medicine
14
Medical model that proposes the
customization of healthcare, with medical
decisions, practices, or products being
tailored to the individual patient
15. Precision Medicine
15
Stratified Medicine
Targeted Therapy
Personalized Medicine
P4 Medicine:
Personalized, Predictive,
Preventive, Participatory
Medical model that proposes the
customization of healthcare, with medical
decisions, practices, or products being
tailored to the individual patient
16. iASiS: Vision and Objectives
16
iASiS Vision:
Turn clinical, pharmacogenomics, and other Big Data into actionable
knowledge for personalized medicine and health policy-making
iASiS Objectives:
•Integrate automated unstructured and structured data analysis,
image analysis, and sequence analysis into a Big Data framework
•Use the iASiS framework to support personalized diagnosis and
treatment
22. Pilot 1: Lung Cancer
22
Motivation:
• Lung cancer among the most
• common and deadly diseases
• costly cancers
• Lung cancer is a heterogeneous
disease. Characteristics differ among
• patients
• tumor regions
iASiS will enable:
• Discovery of correlations among
tumor spread, prognosis, response to
treatment
• Unraveling molecular mechanisms
that predict response to different
tumor types (signatures)
23. Big Data in the Lung Cancer Pilot
• Pharmacological knowledge extracted
from publicly available datasets
• Biomedical ontologies and taxonomies
• terminology standardization
• semantically describing the EHRs
• EHRs in Spanish
• PET/CT Images
• Genomic Data/Liquid Biopsy
Samples
24. Pilot 2: Alzheimer's Disease
Motivation:
• Approximately, 10% of people over
65 suffer from Alzheimer’s
• Heterogeneity of the symptoms
impedes accurate diagnosis and
treatments
iASiS will enable:
• Discovery of patterns associated with
prognosis, outcomes, and response
to treatments
• Association of medical and lifestyle
advice to Alzheimer’s risk and stages
of severity
25. • EHRs in English
• MRI Brain Images
• Genomic Data
• Pharmacological knowledge extracted
from publicly available datasets
• Biomedical ontologies and taxonomies
• terminology standardization
• semantically describing the EHRs
Big Data in the Alzheimer's Disease Pilot
26. Clinical Big Data Analytics
Clinical
Notes NLP
Data
Preprocessing
Medical
Images
Deep Learning Predictive
Models
Medical
Vocabularies
Knowledge
Data Mining
Knowledge
Graph
27. Genomic Big Data Analytics
Identification of
RNAs regulated
by RBP
Comparison
with available
information
Integration with
transcriptomic
data
Hospital-derived
data
Knowledge
Graph
Identification of
key genes and
interactions
28. Open Big Data Analytics
•Heterogeneous open data
•Semantic indexing of the data via ontologies and thesauri
•Knowledge extraction from the data
• NLP and network analysis technologies
29. Agenda
1. iASiS: Big Data to Support Precision Medicine and Public
Health Policy
2. The iASiS Architecture
3. Big Data Management and Analytics in iASiS
29
33. Agenda
1. iASiS: Big Data to Support Precision Medicine and Public
Health Policy
2. The iASiS Architecture
3. Big Data Management and Analytics in iASiS
33
40. Ontario: Evaluation Study
40
Benchmark by Ali Hasnain et. al BioFed: federated query processing over
life sciences linked open data. Journal of Biomedical Semantics 2017.
RDF Dataset Name Number of RDF Triples
Chebi 4,772,706
DrugBank 517,023
Kegg 1,090,830
Affymetrix 44,207,146
Dailymed 162,972
Diseasome 72,445
Sider 101,542
Medicare 44,500
LinkedCT 9,804,652
Linked TCGA-A 35,329,868
41. Ontario: Evaluation Study
41
Benchmark by Ali Hasnain et. al BioFed: federated query processing over
life sciences linked open data. Journal of Biomedical Semantics 2017.
Category #Triple Patterns #Star-Shaped
Subqueries
#Union #Optional
Min Max Min Max Min Max Min Max
Simple
Queries
3 8 2 4 0 1 0 1
Complex
Queries
6 12 2 5 0 1 0 1
42. Ontario: Evaluation Study
42
• Ontario is compared with
state-of-the-art federated
query processing tools
(ANAPSID and FedX) and direct
SPARQL endpoint
• Ontario exhibits better
performance than the results
of the engines in terms of
throughput
43. Ontario: Evaluation Study
43
• Ontario is compared with
state-of-the-art federated
query processing tools
(ANAPSID and FedX) and direct
SPARQL endpoint
• Ontario exhibits better
performance than the results
of the engines in terms of
throughput
44. Big Data Analytics and Pattern Discovery
44
Pragmatics
ContextSemantics
Drug
Similarity
Target
Similarity
Detecting
Patterns
Predicting
Interactions
Computing
Similarity
Pattern Detection Prediction
Principle
Network of
Drugs, Targets,
and interactions
Patterns of
similar Drugs,
similar Targets,
and their
interactions
Discovered
Drug-Target
Interactions
ChEBI Ontology
46. Patterns between Drug and Side Effects
46
Predicted Interactions between Drugs and Side Effects
47. Drug-Target Interaction Discovery
47
Nuclear
Receptor GPCR
Ion
Channel
Enzym
e
Drugs 54 223 210 445
Targets 26 95 204 664
Interactions 90 635 1,476 2,926
Avg. Interaction per
Target 3.46 6.68 7.23 4.4
Avg. Interaction per
Drug 1.66 2.84 7.02 6.57
48. Supervised Machine Learning Approaches
48
Machine Learning for Drug-Target Interaction Discovery:
• BLM: Bipartite Local Method [Cheng et al]
• LapRLS: Laplacian Regularized Least Squares [Xia et al]
• GIP: Gaussian Interaction Profile [Van Laarhoven et al]
• KBMF2K: Kernelized Bayesian Matrix Factorization with
twin Kernels [Gonen]
• NBI: Network-Based Inference [Cheng et al]
49. Unsupervised Approaches
49
• semEP: Graph Partition into communities from where new
interactions are predicted [Palma, Vidal, and Raschid]
• Metis: Multilevel recursive-bisection [George Karypis and
Vipin Kumar]
• Ncut: Normalized Cuts [Shi and Malik]
50. Drug-Target Interaction Discovery
50
Top5 Novel interactions are interactions that do not appear in
the dataset and can be validated in STITCH
(http://stitch.embl.de/) and KEGG (http://www.kegg.jp )
Method
Nuclear
Receptor GPCR
Ion
Channel Enzyme
semEP 5 5 4 1
Metis 3 5 2 1
Ncut 3 4 1 1
BLM 2 1 0 0
NBI 1 1 1 2
GIP 4 2 1 3
LapRLS 4 4 2 2
KBMF2K 4 4 4 4
51. Conclusions and Future Work
51
Biomedicine Big Data The iASiS Pipeline
The iASiS Architecture
Big Data Management and Analytics
52. Conclusions and Future Work
52
Biomedicine Big Data The iASiS Pipeline
The iASiS Architecture
Big Data Management and Analytics
Next Steps:
● Collect Big Data
● Extract Knowledge from Big Data sources
● Create the iASiS Knowledge Graph
● Enforce Data Access and Privacy Policies
● Big Data Processing and Analytics