Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Dec 1, 2019

Abstract

OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

Authors

Alison E Fohner

Division of Research, Kaiser Permanente, Oakland, California, USA.
John D Greene

Division of Research, Kaiser Permanente, Oakland, California, USA.
Brian L Lawson

Division of Research, Kaiser Permanente, Oakland, California, USA.
Jonathan H Chen

Stanford Center for Biomedical Informatics Research, Stanford, CA.
Patricia Kipnis

Division of Research, Kaiser Permanente, Oakland, California, USA.
Gabriel J Escobar
Vincent X Liu

3 Division of Research, Kaiser Permanente, Oakland, California.

Keywords

Adult Aged Aged, 80 and over Anti-Bacterial Agents Electronic Health Records Female Hospital Mortality Hospitalization Humans Male Middle Aged Patient Acuity Quality of Health Care Retrospective Studies Sepsis Unsupervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (31314892)

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals