Difference between revisions of "Eighth Clinical and Translational Science Ontology Workshop"

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''Working session on the ontology of social determinants of health''
 
''Working session on the ontology of social determinants of health''
  
9:00 Clint Dowling, "[https://buffalo.box.com/v/Dowling-Categories Social Categories as Cognitively Represented Person Aggregates
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9:00 Clint Dowland, "[https://buffalo.box.com/v/Dowling-Categories Social Categories as Cognitively Represented Person Aggregates
  
10:45 Max Diller, "[https://buffalo.box.com/v/Occuptions-Diller What We Mean When We Talk About Occupations]"  
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10:45 Matt Diller, "[https://buffalo.box.com/v/Occuptions-Diller What We Mean When We Talk About Occupations]"  
 
:We discuss two topics of ongoing work motivated by key SDoH use cases: (1) occupation and (2) social categories such as ethnic and gender categories
 
:We discuss two topics of ongoing work motivated by key SDoH use cases: (1) occupation and (2) social categories such as ethnic and gender categories
  

Revision as of 21:27, 17 March 2022

Announcement

The Clinical and Translational Science Ontology Group (CTSOG) invites you to join us March 16-18, 2022 in Orlando, Florida to discuss AI, Complex Systems in Biomedicine, and the role of ontology both in tempering the expectations of AI and advancing it to goals it can achieve. For example, we hear things all the time like “Google’s deep-learning program for determining the 3D shapes of proteins stands to transform biology, say scientists.” An optimism of this sort as to the potential of AI is shared by many working in the field of clinical and translational science. The purpose of this workshop is to explore the basis for this optimism, by looking at successes and failures of AI in different areas of biomedicine.

Example topics include:

  • Can NLP build useful medical ontologies?
  • Machine learning and the role of ontology
  • AI and team science
  • AI and medical diagnosis and clinical decision support
  • The limits of AI when applied to complex systems
  • Complex systems and the replication crisis
  • AI in therapeutic decision making
  • AI in modelling cell biology
  • AI in modelling systems biology
  • AI-driven cell ontology
  • The challenges of implementing AI in healthcare

Persons who are interested in participating can register here. If you have any questions, please contact William Hogan at hoganwr@ufl.edu.


Organizers

Workshop Co-organizers:

Bill Hogan, Jobst Landgrebe, Barry Smith

CTSOG Co-chairs:

Bill Hogan (University of Florida College of Medicine, Gainesville, FL), hoganwr@ufl.edu

Barry Smith (University at Buffalo, Buffalo, NY), phismith@buffalo.edu

Past meetings of CTSOG

Sponsors

  • University of Florida Clinical and Translational Science Institute Biomedical Informatics Program

Date

March 16 (Wednesday) - 18 (Friday), 2022

Venue

Embassy Suites by Hilton Orlando Lake Buena Vista South, Kissimmee, FL (20 minutes from Orlando airport).

To reserve a room in our block, click here. The room rate is available 3 days pre and post meeting in case you'd like to stay in Florida and enjoy the warm weather a little longer.

Registration

Registration is free, but we absolutely need you to register for planning purposes.

To register for the meeting, click here.

Schedule

Outline of Agenda (starting and stopping times for official agenda each day in bold)

Tuesday March 15th

Pre-Workshop Informal Meet & Greet: We will meet between 7pm and 10pm ...

Wednesday March 16th

Working session on the ontology of social determinants of health

9:00 Clint Dowland, "[https://buffalo.box.com/v/Dowling-Categories Social Categories as Cognitively Represented Person Aggregates

10:45 Matt Diller, "What We Mean When We Talk About Occupations"

We discuss two topics of ongoing work motivated by key SDoH use cases: (1) occupation and (2) social categories such as ethnic and gender categories

1:15-3:15 AI and the Ontology of Complex Systems

Abstract: “Google’s deep-learning program for determining the 3D shapes of proteins stands to transform biology, say scientists.” An optimism of this sort as to the potential of AI is shared by many working in the field of clinical and translational science. The purpose of this tutorial is to explore the basis for this optimism, by looking at successes and failures of AI in different areas of biomedicine.

Potential topics to be explored are:

- Can NLP build useful medical ontologies?
- Machine learning and the role of ontology
- AI and team science
- AI and medical diagnosis and clinical decision support
- The limits of AI when applied to complex systems
- Complex systems and the replication crisis
- AI in therapeutic decision making
- AI in modelling cell biology
- AI in modelling systems biology
- AI-driven cell ontology
- The challenges of implementing AI in healthcare

The tutorial will focus on the question of what domains of medicine will benefit most from AI and how AI will impact the way we work in research and medical care.

Jobst Landgrebe is the founder and managing director of Cognotekt, an AI company based in Cologne, Germany, focusing on the creation of structured data from natural language text. Dr Landgrebe is an MD with a background in biomedical informatics. He is the co-author, with Barry Smith, of Why Machines Will Never Rule the World. Artificial Intelligence without Fear, to be published by Routledge in summer 2022.

3:30-5:00 Sivaram Arabandi, Clinical Decision Support, AI, and Ontology

Abstract: In an increasingly polychronic patient population, acute and/or new diseases (e.g. Covid) present a distressing reality of our limitations in providing high quality clinical care. A key challenge for Clinical Decision Support has been in building executable clinical guideline models that can interpret patient data and provide insights at the point of care. In this talk, the CPG-on-FHIR open standards based framework for creating shareable and reusable Clinical Practice Guidelines (CPG) is described.

5:30p Reception

Thursday March 17th

Presentations with breaks and lunch provided

Educational attainment is a key social determinant of health. I will report on classes developed in the Ontology of Medically Related Social Entities (OMRSE), where we defend a view that education imparts both knowledge (as an ICE) and the neurologically-based skills (some motor, some more intellectual) to apply it.
The 'replication problem' is a phrase to describe the inability of scientific communities to independently confirm the results of scientific work. It has plagued medicine as a positive science since its beginnings (Virchov and Pasteur). But it has become worse over the last 30 years and has massive consequences for healthcare practice and policy. This talk explains the reasons for the replication problem in medicine and why it is here to stay.
  • 11:00 Coffee
  • 11:15 Amelia Kahn, Definitions of Uncertainty
A preliminary survey of meanings assigned by government agencies to estimative words in the domain of probability and statistics.
  • 12:15 Lunch
Part 1 provides a general theory of capability as a universal intermediate between function and disposition in BFO. A capability is defined as a disposition in whose realisation some organism or group of organisms has an interest.
Part 2 develops an ontology of language according to which a language is a capability of a linguistic community. The theory is tested in application to dialects phenomena.
  • 14:00 Clint Dowland, Language Capabilities and Clinical Demographics
Matters of money constitute an entire category of social determinants of health. To understand them, we first wish to define 'money'.
  • 16:00 Jihad Obeid, Phenotyping Using Deep Learning Text Classification: Can Ontologies Play a Role?
The use of electronic health records (EHR) to identify specific clinical phenotypes has gained significant momentum over recent years. A variety of natural language processing pipelines leverage ontologies for of clinical text annotation and information extraction. With the advent of vast computational power, significant strides have been made in deep learning approaches. During this presentation, we will discuss various use cases of clinical text classifiers, using both traditional machine learning algorithms and deep learning. We will examine the performance and utility of these models for both phenotyping and predictive tasks in a variety of clinical scenarios as well as the impact of pre-trained simple language models. Future approaches using more advanced language models and ontologies will be considered.

Friday March 18th

9a-12p Working session and discussion of next steps, closing

  • Discussion and next steps

Rationale/Goals

Travel Fund Application

Participants

  • Barry Smith, co-organizer, University at Buffalo
  • Jobst Landgrebe, co-organizer and special guest
  • William Hogan, co-organizer, University of Florida
  • Sivaram Arabandi, Ontopro
  • Ravi Bajracharya, datum.md
  • Jiang Bian, University of Florida
  • Sarah Bost, University of Florida
  • Naomi Braun, University of Florida
  • Matt Diller, University of Florida
  • Clint Dowland, University of Florida
  • Bill Duncan, University of Florida
  • Yi Guo, University of Florida
  • Hank Head, Optum Inc.
  • Amelia Kahn, University at Buffalo
  • Alex Loiacono, University of Florida
  • Jihad Obeid, Medical University of South Carolina
  • Samson Tu, Stanford University
  • Donny Weinbrenner, University of Florida
  • Pengfei Yin, University of Florida