The document discusses openmhealth, a project to create an open architecture for mobile health data. It describes the goals of openmhealth including being a convener, scaling technologies, and flipping the direction of research inference. The architecture includes data processing units (DPUs) and data visualization units (DVUs) with standardized APIs. It also discusses a personal evidence architecture and engaging developers and health innovators to build on the openmhealth codebase and co-develop use cases.
mHealth Israel_Digital Garage_Open Newtork Lab_Japan Digital OverviewLevi Shapiro
Presentation for mHealth Israel by Takahiro Shoji, Managing Director, Digital Garage, Japan's most respected venture capital investor with 20+ years of investment success, including Twitter, LinkedIn, Glide, etc.
The Innovation Doctor Is In: How SMART on FHIR Will Evolve EHRsMedullan
With the shift to population health moving into high gear, standards are needed to create and support new workflows and support tools for clinicians. Clinicians don't need "yet another system". EHR deployments are painful enough as it is! SMART on FHIR offers the opportunity to bring new innovations from the Health IT community into the existing EHR systems where everyone wins: clinicians, Health IT innovators, EHR systems, and Hospital and Health Systems. It's simple, it's open, and allows companies like Medullan to bring human-centered technology solutions to an ecosystem intended to take better care of humans.
Join us to find out how much of that promise is available today and why SMART on FHIR may or may not become the defacto standard in the future.
Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Depression Detection in Tweets using Logistic Regression Modelijtsrd
In the growing world of modernization, mental health issues like depression, anxiety and stress are very normal among people and social media like Facebook, Instagram and Twitter have boosted the growth of such mental health. Everything has its legitimacy and negative mark. During this pandemic, people are more likely to suffer from mental health issues, they are available 24 7 and are cut off from the real world. Past examinations have shown that individuals who invest more energy via online media are bound to be depressed. In this project, we find out people who are depressed based on their tweets, followers, following and many other factors. For this, I have trained and tested our text classifier, which will distinguish between the user who is depressed or not depressed. Rahul Kumar Sharma | Vijayakumar A "Depression Detection in Tweets using Logistic Regression Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41284.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-miining/41284/depression-detection-in-tweets-using-logistic-regression-model/rahul-kumar-sharma
(I’ll GO OVER STEP BY STEP IN CLASS TOMORROW)Part OneP.docxgertrudebellgrove
(I’ll GO OVER STEP BY STEP IN CLASS TOMORROW)
Part One
Portfolio Critique Using Morningstar.com
Morningstar, Inc. is a leading provider of independent investment research in the United States and in major international markets and offers an extensive line of Internet, software, and print-based products for individual investors, financial advisors, and institutional clients. Morningstar is a trusted source for insightful information on stocks, mutual funds, variable annuities, closed-end funds, exchange-traded funds, separate accounts, hedge funds, and 529 college savings plans.
1. Go to www.morningstar.com. Sign up for Premium Membership. You will be able to receive a 14-day free trial. Browse the site to become familiar with everything Morningstar has to offer. Be prepared to participate in classroom discussion and bring your questions if you have any.
2. Go to X-Ray and print the page. Write a portfolio critique.
Part Two
Use the daily data on the portfolio returns and the market returns (e.g., the S&P 500 index) to estimate a single-index market model. Your analysis should include
(Morningstar automatically will calculate)
1. Standard deviation for each portfolio.
1. Covariance between the rates of return of portfolio and S&P500.
1. The correlation coefficient between each portfolio and S&P500.
1. Run a regression of each portfolio against the market return and find:\
(In fact Morningstar will automatically calculate)
0. Alpha for each portfolio.
0. Beta for each portfolio.
0. What is the systematic and nonsystematic risk of the each security?
0. Sharpe Ratio of portfolios
1. Plot the risk and return of each portfolio and draw the efficient frontiers.
1. Identify which portfolio dominates on the efficient frontier.
1. For which portfolio had an average return in excess of that predicated by the CAPM?
Essay Portion Study Guide
Psych 120, Spring 2019
1. What are aphantasia (and hyperphantasia), and why are they interesting to conceptualization researchers? What sort of information have we already discovered through studying aphantasia? Discuss TWO experiments we covered in class that could be re-examined in an aphantasic population, and why they would contribute to a greater understanding of cognition.
2. How do we recognize and categorize objects? Trace the processes involved with object recognition and categorization, discussing all possibilities covered for how we can do this. Lastly, provide TWO pieces of evidence in support of those various possibilities.
3. What is the dual visual system theory and what does it have to do with consciousness and cognition? Provide TWO pieces of evidence (neurological or behavioral) supporting the dual visual system theory. Next, discuss how those same TWO pieces of evidence might actually not support the dual visual system theory.
4. How do video games impact cognition? Are all video games equal in their benefits or detriments to various cognitive activities? Provide TWO pieces of evi ...
mHealth Israel_Digital Garage_Open Newtork Lab_Japan Digital OverviewLevi Shapiro
Presentation for mHealth Israel by Takahiro Shoji, Managing Director, Digital Garage, Japan's most respected venture capital investor with 20+ years of investment success, including Twitter, LinkedIn, Glide, etc.
The Innovation Doctor Is In: How SMART on FHIR Will Evolve EHRsMedullan
With the shift to population health moving into high gear, standards are needed to create and support new workflows and support tools for clinicians. Clinicians don't need "yet another system". EHR deployments are painful enough as it is! SMART on FHIR offers the opportunity to bring new innovations from the Health IT community into the existing EHR systems where everyone wins: clinicians, Health IT innovators, EHR systems, and Hospital and Health Systems. It's simple, it's open, and allows companies like Medullan to bring human-centered technology solutions to an ecosystem intended to take better care of humans.
Join us to find out how much of that promise is available today and why SMART on FHIR may or may not become the defacto standard in the future.
Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Depression Detection in Tweets using Logistic Regression Modelijtsrd
In the growing world of modernization, mental health issues like depression, anxiety and stress are very normal among people and social media like Facebook, Instagram and Twitter have boosted the growth of such mental health. Everything has its legitimacy and negative mark. During this pandemic, people are more likely to suffer from mental health issues, they are available 24 7 and are cut off from the real world. Past examinations have shown that individuals who invest more energy via online media are bound to be depressed. In this project, we find out people who are depressed based on their tweets, followers, following and many other factors. For this, I have trained and tested our text classifier, which will distinguish between the user who is depressed or not depressed. Rahul Kumar Sharma | Vijayakumar A "Depression Detection in Tweets using Logistic Regression Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41284.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-miining/41284/depression-detection-in-tweets-using-logistic-regression-model/rahul-kumar-sharma
(I’ll GO OVER STEP BY STEP IN CLASS TOMORROW)Part OneP.docxgertrudebellgrove
(I’ll GO OVER STEP BY STEP IN CLASS TOMORROW)
Part One
Portfolio Critique Using Morningstar.com
Morningstar, Inc. is a leading provider of independent investment research in the United States and in major international markets and offers an extensive line of Internet, software, and print-based products for individual investors, financial advisors, and institutional clients. Morningstar is a trusted source for insightful information on stocks, mutual funds, variable annuities, closed-end funds, exchange-traded funds, separate accounts, hedge funds, and 529 college savings plans.
1. Go to www.morningstar.com. Sign up for Premium Membership. You will be able to receive a 14-day free trial. Browse the site to become familiar with everything Morningstar has to offer. Be prepared to participate in classroom discussion and bring your questions if you have any.
2. Go to X-Ray and print the page. Write a portfolio critique.
Part Two
Use the daily data on the portfolio returns and the market returns (e.g., the S&P 500 index) to estimate a single-index market model. Your analysis should include
(Morningstar automatically will calculate)
1. Standard deviation for each portfolio.
1. Covariance between the rates of return of portfolio and S&P500.
1. The correlation coefficient between each portfolio and S&P500.
1. Run a regression of each portfolio against the market return and find:\
(In fact Morningstar will automatically calculate)
0. Alpha for each portfolio.
0. Beta for each portfolio.
0. What is the systematic and nonsystematic risk of the each security?
0. Sharpe Ratio of portfolios
1. Plot the risk and return of each portfolio and draw the efficient frontiers.
1. Identify which portfolio dominates on the efficient frontier.
1. For which portfolio had an average return in excess of that predicated by the CAPM?
Essay Portion Study Guide
Psych 120, Spring 2019
1. What are aphantasia (and hyperphantasia), and why are they interesting to conceptualization researchers? What sort of information have we already discovered through studying aphantasia? Discuss TWO experiments we covered in class that could be re-examined in an aphantasic population, and why they would contribute to a greater understanding of cognition.
2. How do we recognize and categorize objects? Trace the processes involved with object recognition and categorization, discussing all possibilities covered for how we can do this. Lastly, provide TWO pieces of evidence in support of those various possibilities.
3. What is the dual visual system theory and what does it have to do with consciousness and cognition? Provide TWO pieces of evidence (neurological or behavioral) supporting the dual visual system theory. Next, discuss how those same TWO pieces of evidence might actually not support the dual visual system theory.
4. How do video games impact cognition? Are all video games equal in their benefits or detriments to various cognitive activities? Provide TWO pieces of evi ...
Framework for understanding data science.pdfMichael Brodie
The objective of my research is to provide a framework with which the data science community can understand, define, and develop data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the humanities, sciences, algorithms, and now data science. I augmented it for automated problem-solving with (methods, technology, community) [1][2]. The resulting data science reference framework is used to define the data science knowledge discovery paradigm in terms of the philosophy of data science addressed in [1] and the data science problem-solving paradigm, i.e., the data science method, and the data science problem-solving workflow, addressed in [2][3]. The framework is a much called for unifying framework for data science as it contains the components required to define data science. For insights to better understand data science, this paper uses the framework to define the emerging, often enigmatic, data science problem-solving paradigm and workflow, and to compare them with their well-understood scientific counterparts – scientific problem-solving paradigm and workflow.
The objective of my current research [4] is to develop a 21st C re-conception of data. Unlike 20th C data that are assets, 21st C data science data is phenomenological – a resource in which to discover phenomena and their properties, previously and otherwise impossible.
[1] Brodie, M.L., Defining data science: a new field of inquiry, arXiv preprint https://doi.org/10.48550/arXiv.2306.16177 Harvard University, July 2023.
[2] Brodie, M.L., A data science axiology: the nature, value, and risks of data science, arXiv preprint http://arxiv.org/abs/2307.10460 Harvard University, July 2023.
[3] Brodie, M.L., A framework for understanding data science, arXiv preprint https://arxiv.org/abs/2403.00776 Harvard University, March 2024.
[4] Brodie, M.L., Re-conceiving data in the 21st Century. Work in progress, Harvard University.
Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories.
Why Data Mining?
What Is Data Mining?
Data Mining: On What Kind of Data?
Data Classification
What is Sentiment Classification?
Importance of Sentiment classification
Twitter for Sentiment Classification
Problem Statement
Goal of this Classifications
Method to be used
Conclusion
Review on Solar Power System with Artificial Intelligenceijtsrd
A constant and solid supply of power is essential for the working of the present current and advanced society. A large portion of the exertion in control frameworks investigation has gotten some distance from the system of formal scientific demonstrating which originated from the territories of tasks look into, control hypothesis, and numerical examination to the less thorough and less tedious methods of artificial intelligence AI . AI Methods have turned out to be popular for taking care of various issues in control frameworks like control, arranging, forecast, scheduling, and so forth. These strategies can manage troublesome assignments looked by applications in present day extensive power frameworks with significantly more interconnections introduced to meet the increasing load demand. The real goal of this paper is to show how computerized reasoning procedures may assume an essential part in displaying and expectation of the execution of sun based vitality frameworks. The paper traces a comprehension of how expert systems and neural systems work by method for exhibiting various issues in the diverse orders of sun based vitality designing. Prof. Vijay Aithekar | Mr. Hitesh "Review on Solar Power System with Artificial Intelligence" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47866.pdf Paper URL: https://www.ijtsrd.com/physics/engineering-physics/47866/review-on-solar-power-system-with-artificial-intelligence/prof-vijay-aithekar
Introduction to Data Analysis Course Notes.pdfGraceOkeke3
"Embark on a journey into data analysis with our Introduction to Data Analysis slides. Uncover the fundamentals and prerequisites for effective analysis, explore types of data, and discover essential tools and methodologies. Equip yourself with the skills to unlock valuable insights.
Big Data, Computational Biology & the Future of Strategic Planning for ResearchNBBJDesign
The advent of computational biology in the era of “big data” is triggering a dramatic change in the strategic capital planning process and metrics for space allocation and utilization for translational science. In this presentation, Andy Snyder - Principal and NBBJ's Science & Education Practice leader, and Bruce Stevenson, VP of Research Operations at Nationwide Childrens Hospital - chart new relationships between strategic planning, programming, facility planning and scientific workplace features for biomedical research and translational medicine. The presentation sets out new best practices for navigating limited funding resources while preparing for new science directions and workforce needs, research space requirements, and advancements in scientific equipment, and they identify new ways to leverage data, metrics, analytical processes, and tools for improved program/infrastructure alignment.
Big Data for International DevelopmentAlex Rascanu
Alex Rascanu delivered the "Big Data for International Development" presentation at the International Development Conference that took place on February 7, 2015 at University of Toronto Scarborough.
Outline & Research Design RoadmapThis exercise will help you bui.docxalfred4lewis58146
Outline & Research Design Roadmap
This exercise will help you build off the existing literature you documented in the annotated bibliography and develop a plan for your own research project. Bring this completed document with you to your one on one meeting with Dr. Stevenson or Dr. Delshad on September 30th. Please type your information into this document and print it off.
Student Name:
Research Question:
1) Dependent variable:
A) What is your dependent variable? If you have more than one discuss all dependent variables in you analysis.
B) How have previous researchers measured this variable based on your review of the literature?
C) How will you measure this variable for your study?
D) From where will you obtain the data necessary to measure the variable?
2) Independent variables:
A) What schools of thought did you identity in your annotated bibliography?
B) What independent variables are the key focuses of each of these schools of thought?
C) How do previous researchers measure these variables?
D) How will you measure these variables for your study?
E) From where will you obtain the data necessary to measure the variables?
F) Are there any independent variables you plan to include in your study that are not covered in the current schools of thought listed on your annotated bibliography?
a. If so, seek out information about these variables to incorporate into your literature review, and explain:
i. How do previous researchers measure these variables?
ii. How will you measure these variables for your study?
iii. From where will you obtain the data necessary to measure the variables?
3) What if any major challenges are you having with your research project that you need help with?
PSC 401 – Student Presentation Rubric
1
2
3
4
Mean
Organization
Audience cannot understand presentation because of poor organization; introduction is undeveloped or irrelevant; main points and conclusion are unclear;
Audience has difficulty following presentation because of some abrupt jumps; some of the main points are unclear or not sufficient stressed;
Satisfactory organization; clear introduction; main points are well stated, even if some transitions are somewhat sudden; clear conclusion;
Superb organization; clear introduction; main points well stated and argued, with each leading to the next point of the talk; clear summary and conclusion.
Mechanics
(PowerPoint or other supporting materials)
Slides seem to have been cut-and pasted together haphazardly at the last minute; numerous mistakes; speaker not always sure what is coming next;
Boring slides; no glaring mistakes but no real effort made into creating truly effective slides;
Generally good set of slides; conveys the main points well;
Very creative slides; carefully thought out to bring out both the main points as well as the subtle issues while keeping the audience interested.
Delivery
Mumbles the words, audience members in the back can't hear anything; too many filler words; dist.
This presentation was provided by Kristi Holmes of Northwestern University during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Empathic inclination from digital footprints
Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, Marco de Gemmis and Giovanni Semeraro
University of Bari “Aldo Moro”, Dept. of Computer Science, Italy
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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/
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The objective of my research is to provide a framework with which the data science community can understand, define, and develop data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the humanities, sciences, algorithms, and now data science. I augmented it for automated problem-solving with (methods, technology, community) [1][2]. The resulting data science reference framework is used to define the data science knowledge discovery paradigm in terms of the philosophy of data science addressed in [1] and the data science problem-solving paradigm, i.e., the data science method, and the data science problem-solving workflow, addressed in [2][3]. The framework is a much called for unifying framework for data science as it contains the components required to define data science. For insights to better understand data science, this paper uses the framework to define the emerging, often enigmatic, data science problem-solving paradigm and workflow, and to compare them with their well-understood scientific counterparts – scientific problem-solving paradigm and workflow.
The objective of my current research [4] is to develop a 21st C re-conception of data. Unlike 20th C data that are assets, 21st C data science data is phenomenological – a resource in which to discover phenomena and their properties, previously and otherwise impossible.
[1] Brodie, M.L., Defining data science: a new field of inquiry, arXiv preprint https://doi.org/10.48550/arXiv.2306.16177 Harvard University, July 2023.
[2] Brodie, M.L., A data science axiology: the nature, value, and risks of data science, arXiv preprint http://arxiv.org/abs/2307.10460 Harvard University, July 2023.
[3] Brodie, M.L., A framework for understanding data science, arXiv preprint https://arxiv.org/abs/2403.00776 Harvard University, March 2024.
[4] Brodie, M.L., Re-conceiving data in the 21st Century. Work in progress, Harvard University.
Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories.
Why Data Mining?
What Is Data Mining?
Data Mining: On What Kind of Data?
Data Classification
What is Sentiment Classification?
Importance of Sentiment classification
Twitter for Sentiment Classification
Problem Statement
Goal of this Classifications
Method to be used
Conclusion
Review on Solar Power System with Artificial Intelligenceijtsrd
A constant and solid supply of power is essential for the working of the present current and advanced society. A large portion of the exertion in control frameworks investigation has gotten some distance from the system of formal scientific demonstrating which originated from the territories of tasks look into, control hypothesis, and numerical examination to the less thorough and less tedious methods of artificial intelligence AI . AI Methods have turned out to be popular for taking care of various issues in control frameworks like control, arranging, forecast, scheduling, and so forth. These strategies can manage troublesome assignments looked by applications in present day extensive power frameworks with significantly more interconnections introduced to meet the increasing load demand. The real goal of this paper is to show how computerized reasoning procedures may assume an essential part in displaying and expectation of the execution of sun based vitality frameworks. The paper traces a comprehension of how expert systems and neural systems work by method for exhibiting various issues in the diverse orders of sun based vitality designing. Prof. Vijay Aithekar | Mr. Hitesh "Review on Solar Power System with Artificial Intelligence" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47866.pdf Paper URL: https://www.ijtsrd.com/physics/engineering-physics/47866/review-on-solar-power-system-with-artificial-intelligence/prof-vijay-aithekar
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The advent of computational biology in the era of “big data” is triggering a dramatic change in the strategic capital planning process and metrics for space allocation and utilization for translational science. In this presentation, Andy Snyder - Principal and NBBJ's Science & Education Practice leader, and Bruce Stevenson, VP of Research Operations at Nationwide Childrens Hospital - chart new relationships between strategic planning, programming, facility planning and scientific workplace features for biomedical research and translational medicine. The presentation sets out new best practices for navigating limited funding resources while preparing for new science directions and workforce needs, research space requirements, and advancements in scientific equipment, and they identify new ways to leverage data, metrics, analytical processes, and tools for improved program/infrastructure alignment.
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Outline & Research Design Roadmap
This exercise will help you build off the existing literature you documented in the annotated bibliography and develop a plan for your own research project. Bring this completed document with you to your one on one meeting with Dr. Stevenson or Dr. Delshad on September 30th. Please type your information into this document and print it off.
Student Name:
Research Question:
1) Dependent variable:
A) What is your dependent variable? If you have more than one discuss all dependent variables in you analysis.
B) How have previous researchers measured this variable based on your review of the literature?
C) How will you measure this variable for your study?
D) From where will you obtain the data necessary to measure the variable?
2) Independent variables:
A) What schools of thought did you identity in your annotated bibliography?
B) What independent variables are the key focuses of each of these schools of thought?
C) How do previous researchers measure these variables?
D) How will you measure these variables for your study?
E) From where will you obtain the data necessary to measure the variables?
F) Are there any independent variables you plan to include in your study that are not covered in the current schools of thought listed on your annotated bibliography?
a. If so, seek out information about these variables to incorporate into your literature review, and explain:
i. How do previous researchers measure these variables?
ii. How will you measure these variables for your study?
iii. From where will you obtain the data necessary to measure the variables?
3) What if any major challenges are you having with your research project that you need help with?
PSC 401 – Student Presentation Rubric
1
2
3
4
Mean
Organization
Audience cannot understand presentation because of poor organization; introduction is undeveloped or irrelevant; main points and conclusion are unclear;
Audience has difficulty following presentation because of some abrupt jumps; some of the main points are unclear or not sufficient stressed;
Satisfactory organization; clear introduction; main points are well stated, even if some transitions are somewhat sudden; clear conclusion;
Superb organization; clear introduction; main points well stated and argued, with each leading to the next point of the talk; clear summary and conclusion.
Mechanics
(PowerPoint or other supporting materials)
Slides seem to have been cut-and pasted together haphazardly at the last minute; numerous mistakes; speaker not always sure what is coming next;
Boring slides; no glaring mistakes but no real effort made into creating truly effective slides;
Generally good set of slides; conveys the main points well;
Very creative slides; carefully thought out to bring out both the main points as well as the subtle issues while keeping the audience interested.
Delivery
Mumbles the words, audience members in the back can't hear anything; too many filler words; dist.
This presentation was provided by Kristi Holmes of Northwestern University during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
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University of Bari “Aldo Moro”, Dept. of Computer Science, Italy
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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/
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1. IDA SIM, CO-FOUNDER DEBORAH ESTRIN, CO-FOUNDER DAVID HADDAD, PROGRAM MANAGER December 5, 2011 #openmhealth Sponsored by the Robert Wood Johnson Foundation and the California Health Care Foundation A project of the Tides Center
12. infovis architecture INFOVIS IS A SET OF TOOLS FOR ANALYSIS AND VISUALIZATION OF INFORMATION ARCHITECTURE IMPLIES SYSTEMIC, STRUCTURAL, AND ORDERLY PRINCIPLES TO MAKE SOMETHING WORK (SAUL WURMAN) BY DRIVING THE INFOVIS ARCHITECTURE THROUGH CONCRETE APPLICATIONS WE ’RE ABLE TO CATALYZE RAPID EVOLUTION, VALIDATION, AND SHARING OF SENSEMAKING TECHNIQUES
Thank you very much for coming to our session. HASHTAG for the session is #OPENMHEALTH
I'm sure most of you have seen this Gartner Hype Cycle graph before. Where would you say mHealth is right now? Raise your hand. Here (just before or at the peak)? Here (after the peak)? Or here (in the trough)? Yes, we will go through the Trough of Disillusionament, but the real question is where this will all end up. What will be mHealth's plateau of productivity?
Let's start with a very high level view of mHealth. mHealth applications are sources of passive and actively collected data, which must be visualized and interpreted, and integrated into daily life and clinical care. It is the mHealth data that are the nuggets of value here...
...data to drive what we have identified as three essential feedback loops to improve health outcomes.
First is data to provide feedback on self-care. How is this medication working for me? Is my stress better now that I'm sleeping more? A second feedback loop is to clinical care. For clinicians like me, I want to know how my patients are doing: Is Mrs. Lee's depression improving? Perhaps I might want to intervene in her own feedback loop with advice, a dosage change, or some other action. Finally, data can act as research evidence, driving a loop of knowledge to tell us what works and what doesn't in different contexts, which in turn will propagate back to these other feedback loops. But these feedback loops are not happening as well or as fast we need . mHealth data is really a new kind of data that we've never gotten our hands on before: large volume, real-time, self-reported data collected with high frequency. This data tends to have lots of bias, noise, and gaps, so it's hard to make SENSE of it, to extract relevant features and patterns to drive the feedback loops. As an ecosystem, mHealth is struggling to develop and disseminate tools and techniques for sensemaking of mHealth data.
...and without better sensemaking to drive the 3 essential feedback loops
we are likely to end up not at the fabled plateau of productivity, but rather at a plateau of diminished promise. This opportunity gap is what OpenmHealth is about . We seek to tip the mHealth ecosystem to this trajectory, rather than this one. How are we going to do that?
・ Today, mHealth apps are being built independently, with little sharing of data, methods, or learning. We believe that this siloed mHealth ecosystem is a barrier to the promise of mHealth . We believe that what is needed is to borrow the Internet ’s approach of open modular sharing and learning. ・ The Internet has what is called an hourglass architecture, from which it derived much of its success. There is a common protocol that acts as a simple point of commonality at the narrow waist. This allows innovation to flourish through open interfaces, or APIs, both above and below the waist. ・ Open mHealth aims to catalyze the mHealth ecosystem from a siloed architecture to an hourglass architecture, by developing shared components and open APIs at the narrow waist. This should in crease the scale and effectiveness of mHealth sensemaking for self-care, clinical care, and research evidence, and ultimately to improve health outcomes.
I will now hand the floor over to Deborah to introduce our architecture, and talk about our InfoVis components for sensemaking. I will come back to describe our plans for a personal evidence architecture to tie sensemaking to generating research evidence.and David will finish up describing our project activities and how you can get involved.
no shortage of good ‘apps for that’ need progress top of hourglass...“Sense making” -- tools and techniques for extracting and evaluating data to drive feedback loops (patient self-care, clinical care, research. ) Goal: vibrant decentralized community of openmhealth users and contributors (tech and health innovator sectors) Note: This is not about agreeing on interoperability standards, we dont have to all agree, rather its about creating and sharing pluggable software/algorithms.
no shortage of good ‘apps for that’ need progress top of hourglass...“Sense making” -- tools and techniques for extracting and evaluating data to drive feedback loops (patient self-care, clinical care, research. ) Goal: vibrant decentralized community of openmhealth users and contributors (tech and health innovator sectors)
Infovis shorthand for sensemaking software modules--data analysis and presentation techniques--reusable, combinable, across mhealth apps Decentralized innovative community needs architecture so independently developed software modules can be mixed and matched--and minimal interface definitions that all can depend on Architecture : small set of common principles/practices by which these modules are described and interfaced to one another. Architecture is why we have the internet we do Need to drive architecture development w/ real use case of patient-facing mHealth--PTSD w/ VA
Photo: VA and DOD. Adapted from Julia Hoffman (VA), et al. Julia Hoffman et al. PTSD Coach, personal tool to manage and mitigate symptoms. Stigma and logistical challenges of seeking help. Full week between sessions to self-manage. Skills for between-session and independent self-management: self-assess variations in condition; develop portable skills to address acute symptoms Developed 2010 and on app store mid 2011
PTSD Coach implemented as a stand alone application for patient self-care. PTSD explorer brings data to clinician to support treatment process PTSD explorer plumbs application to capture data on tool participation, symptom self reports, support types, coping and medications---data are byproducts of use Building Infovis data processing modules that extract features from data for clinician -- trends, correlations over time, across parameters, with noisy gappy data Enable iterative innovation for clinician. Not data exploration during your patients all too short session but facilitated flexible config of dataviews for clinician, cohort, condition Enable continuous iterative improvement itself informed by analytics....why should websites be more scientific and data driven than clinical practice?
same process, arch, tools for many patient facing mhealth applications overall architecture elements: The third party data applications and stores -- mHealth applications store and manage data, -- this is where ptsd coach and explorer sit data processing units (DPUs) building blocks for extracting relevant features from data streams, Data Visualization building blocks for creating presentations of data, Infographics that might be created specially to tailor the look of the user interface, and a local cache for performance. Goal-- support distributed community building applications with best available software components. not a data repository, not hosting the data, not THE common software platform running on mobile devices openmhealth community members will integrate software modules into their own platforms. **complementing and integrating with their own "secret sauce" components. modularity facilitates adapted, modified, personalized data views as clinical practice evolves All about economy of scale--jointly advance general purpose components--advance in function, accessibility, usability, affordability the way our web tools have , rather than at the pace of traditional siloed clinical tool
Josh Selsky chief software architect; informal community advising; initial core developer team jeroen ooms and mark schwartz Modularity--each DPU does one task...compose for higher functions. rough consensus and running code not a priori standard setting---define DPUs data input and output formats as part of the DPU interface. well-documented interfaces allow chaining DPUs to create higher level, correlated features -- F(g(h))....for example, noisy gappy data
data visualization can be modularized and reused across applications combine to create specific interactive, configurable data views -- zoomable timelines, maps not trying to be mhealth service or portal…DVUs embeddable in third party mhealth applications Modules that you can integrate into your platform...use to build your repository... monetized innovation happening above these reusable components…resulting volume of visible innovation much greater. built quickly, efficiently w/ other open standard tools -- HTML5 and Javascript
Sensemaking, fine…modularity, fine….why is it so important that we do this open? Goal -- vibrant decentralized community of users...benefit from open architecture...therefore contribute to it. Openmhealth allows individual innovators to avoid wasting resources reinventing the wheel; spend resources on innovations above (new apps, biomarkers, treatments, interventions) and below (wireless health devices) -- net innovation of market enhanced. Pays not to fork--john mattison of kp: discipline makes institution in better shape to adopt external innovations over time. Weber on open source....recommended by karl brown and retweeted by me. components reused by community grow in validity, robustness , efficiency, interoperability--original creators benefit--the reason to not fork.....holds the community together Open has great potential to catalyze innovation and shared learning fueled by evidence generation.
To advance science and practice of mHealth, the sensemaking has to be brought to bear on scientific questions in scientific way- As science advances the feedback loops will be powered by the resulting evidence and models. Pass it over to Ida to talk about the Personal evidence architecture that will help to close and enhance these loops…
・ When we say we want to advance the "science and practice" of mHealth, one of the biggest questions we need to answer is whether something "works" or not. ・ For example, does Text4Baby work?
・ When we say we want to advance the "science and practice" of mHealth, one of the biggest questions we need to answer is whether something "works" or not. ・ For example, does Text4Baby work? That's an imprecise question though. It should be rephrased as a statistical question, about the strength of association between being "exposed" to Text4 Baby and some outcome we care about, like increased breastfeeding. ・ We can be interested whether Text4Baby works at the population level (in teen moms) or whether it works for an individual (does it work for me ).
・ Most of the time in clinical research, for example in a randomized controlled trial, we're asking questions at the population level. ・ So to see if PTSD Coach works, we randomize say 100 people to PTSD Coach or to usual care and see on average if PTSD Coach works. ・ The problem though is that none of us are average – even if PTSD Coach works on average to reduce PTSD symptoms, it might not work *** for me***, or conversely it might not work for the average person but may work great for me.
・ The only formal method for answering "does it work for me " is the N-of-1 study design. ・ In which there's only 1 person, 1 "n" in the study. So I would be randomized first to PTSD Coach, then usual care, and back and forth a few cycles, assessing my PTSD symptoms along the way. ・ This method is not widely known and is statistically complicated, but offers the kind of personal evidence we need to drive systematic but personalized learning across mHealth
O Our Personal Evidence Architecture aims to make it easier to answer questions scientifically, starting with individual-level questions . Patients and clinicians will be able to define a question, set up a study using say an n-of-1 study template, run the study as an app, and on the backend, it will use InfoVis to do the data analysis and feedback as you heard Deborah describe earlier.
* And once we have evidence from many individual n-of-1 studies, we can essentially flip the traditional direction of research inference on its head, aggregating individual-level evidence to get at population-level evidence, rather than the other way around
・ We are focusing now on building out N-of-1 scripting and analysis, and identifying a small library of high-value shared measures. We're looking at some of the measures in NIH's PROMIS project, for example, but there will also be a need to develop and validate measures specifically for the mHealth context, in which self-report data can be collected several times a day rather than once every 3 months like in many traditional measures. ・ For aggregating evidence, we are exploring a shared set of context meta-data tags, to capture the context about the variable and about how the data was collected, for example the OS and version used, the app version, etc. Context meta-data is critical to ensure that data and evidence can make sense together as well as separately
That's a quick tour of our InfoVis and Personal Evidence Architecture work. Now David will tell you about our other project activities and how you can get involved.
We want to pinch the siloed mHealth applications at the waist to create a scalable, open architecture.
We have some successful open source predecessors in our midst. that play an important function in advancing health IT. From OpenMRS’s medical record system to ODK’s data collection tools, each play an important function and role in this advancement.
Today OpenmHealth is needed to advance mHealth by increasing the speed, innovation, exploration, and effectiveness of mHealth sensemaking.
Open mHealth’s mission is to tip the current mHealth ecosystem to achieve greater openness, integration and evidence in order to improve individual and public health.
We’re going to do this by: Catalytically convening an open community to design, develop, test, generate evidence and share their learnings from using the Open mHealth architecture. Allowing innovators and entrepreneurs to focus on their unique market offerings while increasing the validity, robustness and effeciency of shared components and methods. We want mHealth to scale by saving the mHealth community time and $$. Flipping the direction of research inference on its head by generating population-level evidence from personal evidence.
Open mHealth is a 501.3c that just began its work on September 1, 2011. We’re able to achieve our mission through funding from RWJF’s Pioneer Portfolio and the California Healthcare Foundation. We’re honored to be hosted by the Tides Center.
In order to achieve our mission, we are using an integrated approach to bring together developers and health innovators to develop code and drive more use cases along the open mHealth architecture.
Photo: Tapping the Table Board, Fosdem ’ 09, Free and Open Source Software Developers’ European Software Meeting 2009. Creative Commons License These developers we’re referring to include those that work for small startups, technical architects, hackers, and those from the open source community.
Photo: UN Foundation, 2011 Health innovators are those health experts that understand the problem. They own the problem. They might be a clinician, researcher, institution or patient group that has an app or project and want to use the OMH architecture.
These user groups do not suppose that other user groups are not important. Focusing on one group will not benefit OMH or the mHealth space. There’s no benefit to health innovators alone if there’s no one to build solutions for them. Likewise, there’s no benefit to developers if there’s no one to assist in knowledge sharing on specific health subject matters. The intersection of these user groups is critical for anything meaningful to happen in mHealth. We want to break the barriers between developers and health innovators by building an active and productive community.
Technical contributors and users of Infovis modules and other aspects of architecture. INFOVIS EVIDENCE BUSINESS MODEL
We’re going to start monthly Google+ meetups for those interested in discussing the Open mHealth architecture, projects, etc. Go to our website, www.openmhealth.org for email updates about this and other related Open mHealth news.