Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.
OBJECTIVE
Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.


METHODS
Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype.


RESULTS
The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features.


CONCLUSIONS
Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable.


CONCLUSIONS
The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.
Partners HealthCare Personalized Medicine, Boston, MA, USA Brigham and Women's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA syu7@partners.org.
Sheng
S
Yu
Brigham and Women's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA.
Katherine P
KP
Liao
Massachusetts General Hospital, Boston, MA.
Stanley Y
SY
Shaw
Research Computing, Partners HealthCare, Charlestown, MA, USA.
Vivian S
VS
Gainer
Research Computing, Partners HealthCare, Charlestown, MA, USA.
Susanne E
SE
Churchill
Massachusetts Institute of Technology, Cambridge, MA, USA.
Peter
P
Szolovits
Massachusetts General Hospital, Boston, MA Research Computing, Partners HealthCare, Charlestown, MA, USA.
Shawn N
SN
Murphy
Harvard Medical School, Boston, MA, USA Boston Children's Hospital, Boston, MA, USA.
Isaac S
IS
Kohane
Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Tianxi
T
Cai
D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000175:diagnosis; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D017432:Unified Medical Language System