CTS Ontology Workshop 2023
Ontologies, AI and Electronic Health Records
More about the Clinical and Translational Science Ontology Group (CTSOG) and previous meetings.
Feb 23 - 24, 2023 - Charleston, SC
Background
The Clinical and Translational Science Ontology Group (CTSOG) invites you to join us February 23-24, 2023, in Charleston, SC to discuss the role of ontologies in improving electronic health records (EHR) systems by advancing semantic interoperability, translational research, and artificial intelligence (AI). As health data increases in volume and complexity, and artificial intelligence applications gain momentum, the need for careful planning and interoperability becomes more critical. The purpose of this workshop is to explore new paradigms in EHRs, by examining successes and failures of ontologies and AI in different areas of biomedicine and their role in equitable healthcare.
Themes
- Improving the EHR with ontologies and with AI
- The functions of the EHR and other healthcare documents
- Social Determinants of Health
- Mental health
- ChatGPT
Organizers
Workshop Co-organizers
Bill Hogan, Jihad Obeid, 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
Sponsors
- Medical University of South Carolina, the Biomedical Informatics Center and:
Venue
Biomedical Informatics Center
Medical University of South Carolina
Address: 22 Westedge St, Charleston, SC 29403
Directions: From Courtyard Marriott on Lockwood
- 10 minute walk, 0.4 miles, safe crosswalks along the route
- Hotel shuttle – runs periodically, need to ask hotel desk for timing details.
Paid parking:
- Parking deck at 10 WestEdge (building before 22 WestEdge, with Publix on bottom floor)
- App-based parking next to 22 WestEdge building (signage with instructions posted onsite)
Hotel
Courtyard by Marriott Charleston Waterfront
Address: 35 Lockwood Dr, Charleston, SC 29401
Phone: (843) 722-7229
Feb. 24-25 reservation link (use this link if you are extending your stay in Charleston through the weekend)
If you have any issues registering, please call the hotel directly at 843-722-7229 and mention you are visiting for the CTSA Ontology meeting.
Registration
However, registration is required for planning purposes. Please register for the workshop using this form.
Agenda: Thursday Feb 23
08:30a: Breakfast and Networking
09:00: Introduction to the meeting
09:15
- Barry Smith and Jobst Landgrebe: Discussion on AI and Medicine, with a special focus on ChatGPT
Abstract: How are we to explain the peculiar tendency of ChatGPT to throw up what are called ‘hallucinations’? To answer this question we will draw on our book Why Machines Will Never Rule the World, whose core thesis is that an artificial intelligence that could equal or exceed human intelligence—sometimes called "artificial general intelligence" (AGI)—is for mathematical reasons impossible. The argument for this thesis rests on the fact that (1.) human intelligence is a capability of a complex dynamic system—the human brain and central nervous system, and (2.) systems of this sort (like all organic systems) cannot be modelled mathematically in a way that would allow the models to operate inside a computer. We survey on this basis the potential of AI in the future of clinical and translational research.
--break--
11:00
- Discussion and brief presentations on:
- The Clinical and Translational Science ontology landscape in 2023
- Opportunities for AI in medicine
- Will AI make ontologies redundant?
- TBD
12:30-01:30p Networking and Working Lunch
01:30-3:00p Working sessions/Presentations
- Yonghui Wu: A large language model for electronic health records.
Abstract: Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. We develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text). GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks, which can be applied to medical AI systems to improve healthcare delivery.
--break--
3:15-5:00
- Richard Ohrbach and Barry Smith: Defining 'Injury'
Abstract: This session is intended as a contribution to the ontology component of a project for investigating injury and pain. It responds to the NIH TMD IMPACT Collaborative for IMproving PAtient-Centered Translational Research. SNOMED-CT defines an injury as a 'disorder resulting from physical damage to the body'. The WHO defines an injury as a 'bodily lesion at the organic level, resulting from acute exposure to energy (mechanical, thermal, electrical, chemical or radiant), in amounts that exceed the threshold of physiological tolerance.' We will explore these and other definitions with a view to establishing a more coherent understanding of the ontology of injury and of related phenomena such as lesion, trauma, pain, and so forth. Further topics are listed here.
Agenda: Friday Feb 24
09:00-09:30a Breakfast and Networking
09:30-10:45a Working sessions/Presentations
- William Hogan: Semantic Representation of Occupations as Social Determinants of Health.
--break--
11:00-12:30p Working sessions/Presentations
- Justin Reese: Unsupervised machine learning to define subtypes of long COVID using the Human Phenotype Ontology. Co-authors
Abstract: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterized by a wide range of manifestations that are difficult to analyze computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.
We present a method for defining subtypes of long COVID by computationally modeling PASC phenotype data and applying unsupervised machine learning. We extracted Long COVID phenotype data expressed as HPO (Human Phenotype Ontology) terms from electronic healthcare records (EHRs), and then used semantic similarity of phenotype data to calculate a matrix of pairwise patient similarity. This matrix was then used to clustered patients using unsupervised machine learning (k means clustering).
We identified six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centers to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.
Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
12:30-01:30p Networking and Working Lunch
1:00-03:15p Working sessions/Presentations
--break--
03:30-5:00p Working sessions/Presentations of industry partners
Participants
- Barry Smith, co-organizer, University at Buffalo
- William Hogan, co-organizer, University of Florida
- Jobst Landgrebe, Cognotekt, Cologne
- Jihad Obeid, co-organizer, Medical University of South Carolina
- Hamilton Baker, Medical University of South Carolina
- Jobst Landgrebe, University at Buffalo
- Anna Maria Masci, National Institute of Environmental Health Sciences (NIEHS)
- Richard Ohrbach, University at Buffalo
- Justin Reese, Lawrence Berkeley National Laboratory
- Yonghui Wu, University of Florida
