CTS Ontology Workshop 2023: Difference between revisions

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== '''Sponsors''' ==
== '''Sponsors''' ==


* Medical University of South Carolina and the Biomedical Informatics Center
* '''Medical University of South Carolina, the Biomedical Informatics Center''' and:
* Others (coming soon)
 
[[File:Markiii-logo_160.png|link=https://www.markiiisys.com/|Mark III Systems]]              
[[File:Nvidia-logo_180.png|link=https://www.nvidia.com/en-us/|NVIDIA]]              
[[File:TriNetX.png|link=http://trinetx.com/|TriNetX]]


== '''Venue'''==
== '''Venue'''==

Revision as of 22:22, 26 January 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

Special Focus Areas:

  • Social Determinants of Health
  • Mental health
  • EHR across the lifespan

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:

Mark III Systems            NVIDIA            TriNetX

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. 22-23 reservation link

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

Registration is free.

However, registration is required for planning purposes. Please register for the workshop using this form.

Speakers

  • Barry Smith: The Current State of Ontology In Clinical and Translational Science.
  • Justin Reese: Unsupervised machine learning to define subtypes of long COVID using the Human Phenotype Ontology. Authors: Justin T. Reese, Hannah Blau, Elena Casiraghi, Timothy Bergquist, Johanna J. Loomba, Tiffany J. Callahan, Bryan Laraway, Corneliu Antonescu, Ben Coleman, Michael Gargano, Kenneth J. Wilkins, Luca Cappelletti, Tommaso Fontana, Nariman Ammar, Blessy Antony, T.M. Murali, J. Harry Caufield, Guy Karlebach, Julie A. McMurry, Andrew Williams, Richard Moffitt, Jineta Banerjee, Anthony E. Solomonides, Hannah Davis, Kristin Kostka, Giorgio Valentini, David Sahner, Christopher G. Chute, Charisse Madlock-Brown, Melissa A. Haendel, Peter N. Robinson.

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.

  • William Hogan: Semantic Representation of Occupations as Social Determinants of Health.
  • 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.

Agenda

Thursday Feb 23rd

09:00-09:30a Breakfast and Networking

09:30-10:45a Working sessions/Presentations

--break--

11:00-12:30p Working sessions/Presentations

12:30-01:00p Networking and Working Lunch

1:00-03:15p Working sessions/Presentations

--break--

03:30-5:00p Working sessions/Presentations

Friday Feb 24th

09:00-09:30a Breakfast and Networking

09:30-10:45a Working sessions/Presentations

--break--

11:00-12:30p Working sessions/Presentations

12:30-01:00p Networking and Working Lunch

1:00-03:15p Working sessions/Presentations

--break--

03:30-5:00p Working sessions/Presentations

Participants

  • Barry Smith, co-organizer, University at Buffalo
  • William Hogan, co-organizer, University of Florida
  • 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)
  • Justin Reese, Lawrence Berkeley National Laboratory
  • Yonghui Wu, University of Florida