Development of Machine Learning Models to Predict Admission from ED to Inpatient and Intensive Units
2020 Society of Academic Emergency Medicine (SAEM) Southeastern Regional Conference, Greenville, SC.
Results & Outcomes
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Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission
JAMA Network Open 3, no. 2 (February 7, 2020): e1920733.
MLHC 2019 Presentation on Sepsis Watch, Pythia
At the Machine Learning for Healthcare 2019 Conference, the DIHI team presented alongside Dr. Katherine Heller on innovation science and implementation of machine learning for
‘The Human Body Is a Black Box’: Supporting Clinical Decision-Making with Deep Learning
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 99–109. Barcelona Spain: ACM, 2020
Assessing Quality of Surgical Real-World Data from an Automated Electronic Health Record Pipeline
Journal of the American College of Surgeons, January 2020, S1072751520300612.
The Human Body Is a Black Box’: Supporting Clinical Decision-Making with Deep Learning
ArXiv:1911.08089 [Cs], November 18, 2019.
Assessing Quality of Real-World Data Supplied by an Automated Surgical Data Pipeline
Journal of the American College of Surgeons 229, no. 4 (October 25, 2019): S89.
Do no harm: a roadmap for responsible machine learning for health care
Nature Medicine 25, no. 10 (October 2019): 1627.
Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality
MedRxiv, June 26, 2019, 19000133.