Electronic medical record phenotyping using the anchor and learn framework.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Apr 23, 2016

Abstract

BACKGROUND: Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient's electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention.

Authors

Yoni Halpern

Department of Computer Science, New York University, New York, NY, USA dsontag@cs.nyu.edu.
Steven Horng

Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA dsontag@cs.nyu.edu.
Youngduck Choi

Department of Computer Science, New York University, New York, NY, USA.
David Sontag

1 Department of Computer Science, New York University , New York, New York.

Keywords

Area Under Curve Databases as Topic Decision Making, Computer-Assisted Electronic Health Records Emergency Service, Hospital Humans Machine Learning Natural Language Processing Phenotype

External Resources

View on PubMed Access via DOI PubMed (27107443)

Electronic medical record phenotyping using the anchor and learn framework.

Abstract

Authors

Keywords

External Resources

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