Deep Sepsis: a look “under the hood” at the model powering Sepsis Watch
Deep Sepsis is the model that powers the Sepsis Watch solution at Duke University Health System, and beyond. Utilizing the Duke data pipeline for real-time
Results & Outcomes
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Development of Machine Learning Models to Predict Admission from ED to Inpatient and Intensive Units
2020 Society of Academic Emergency Medicine (SAEM) National Conference, Denver, CO.
Uncovering Errors in Transitions from Hospital to Nursing Home: A Video Telehealth Transitions Conference
American Geriatrics Society 2020 Annual Meeting, Long Beach, CA, May 9, 2020
Uncovering Errors in Transitions from Hospital to Nursing Home: A Video Telehealth Transitions Conference
The Society for Post-Acute and Long-Term Care Medicine 2020 Annual Conference, Chicago, IL, April 2, 2020.
Presenting Machine Learning Model Information to Clinical End Users with Model Facts Labels
Npj Digital Medicine 3, no. 1 (March 23, 2020): 41
Integrating Electronic Patient Reported Outcomes Into Clinical Workflows in the Epic® Electronic Health Record
Heather A. Rosett; Kris Herring, PhD; William Ratliff, MBA; Bridget F. Koontz, MD; S. Yousuf Zafar, MD, MHS; Thomas W. LeBlanc, MD, MA, MHS. “Integrating
Cardiogenic Shock Poster Presentation at MLHC 2019
Will Ratliff, Aman Kansal, and Sehj Kashyap present Cardiogenic Shock at Machine Learning for Healthcare Conference 2019 in Ann Arbor, Michigan.
Uncovering Errors in Transitions from Hospital to Nursing Home: A Video Telehealth Transitions Conference
NC ACP, Greensboro, NC, February 28, 2020.
Feasibility of Heart Sound Analysis in Individuals Supported with Left Ventricular Assist Devices
ArXiv:2002.12305 [Eess], February 27, 2020.