Use Case Work Groups
Description
The sepsis risk prediction use case refers to a machine learning-based risk prediction model for sepsis. The goal of the solution is to provide early warning for sepsis cases, allowing for timely intervention and improved patient outcomes.
Timeline
Q4 2024 – Q1 2025
Goals
Develop Responsible AI content that focuses on:
Critical best practice guidance and associated tools & resources
Methods, metrics, benchmarks, and open-source tooling to objectively evaluate responsible use of a sepsis risk prediction solution
Output
Best Practice Guidance:
link to guidance documentation once availableTesting & Evaluation Framework: Available Here
Work Group Leads
Name | Organization |
|---|---|
Han-Chin Shing | Amazon |
Geralyn Miller | Microsoft |
Maryzeh Ghassemi | MIT |
Raj Ratwani, Medstar | Medstar |
Jessica Handley | Medstar |
Sonya Makhni | Mayo Clinic Platform |
Work Group Members
Advent Health
American Heart Association
Aidoc
Amazon
American Society of Clinical Oncology (ASCO)
Amputee Coalition
AWS
Bayesian Health
Best Buy
Boston Children’s
CAP
Centaur Labs
Children’s Hospital of Philadelphia (CHOP)
Cleerly
Cleveland Clinic
Covera Health
CVS
Dosis
Duke
Eko
Ferrum Health
Gesund AI
Glass Health
Google
Greensboro Radiology
Harvard Children’s
Honor Health
Intel
Iodine Software
Iterative Health
IU Health
Johns Hopkins
Johns Hopkins Health Plans
Kaiser Permanente
King and Spalding
Komodo Health
Lucern Health
Massachusetts General
Mayo
Mayo Clinic Platform
Medstar
Memora Health
Memorial Sloan Kettering
Mercy
Microsoft
MIT
Montana State University
Mt. Sinai
National Health Council
Northwestern
OCHIN
Open Evidence
Optum/UHG
Oracle
Parkland
Path AI
Penn Medicine
Providence
Rand Corporation
Sharp Healthcare
Shepard Center
Solventum
Stanford
Tortus
Twin Health
UC Davis
UC Riverside
UNC Health
Unifi AI
United Health Group
UW Health- Pharmacy Services
VA
Viz AI
Wolters Kluwer
Yale
