Authors:
Mahshad Koohi H. Dehkordi
1
;
Navya Martin Kollapally
1
;
Yehoshua Perl
1
;
James Geller
1
;
Fadi Deek
1
;
Hao Liu
2
;
Vipina Keloth
3
;
Gai Elhanan
4
and
Andrew Einstein
5
Affiliations:
1
Ying Wu College of Computing, NJIT, Dr. Martin Luther King Jr. Boulevard, Newark, NJ, U.S.A.
;
2
Department of Computer Science, Montclair State University, 1 Normal Avenue, Montclair, NJ, U.S.A.
;
3
Department of Medical Informatics, Yale University, 51 Prospect Street, New Haven, CT, U.S.A.
;
4
Center for Genomic Medicine, School of Medicine, University of Nevada, 1664 N. Virginia Street, Reno, NV, U.S.A.
;
5
Dept. of Medicine, Cardiology Division, Columbia University Irving Medical Center New York, 168th Street, U.S.A.
Keyword(s):
Highlighting, Electronic Health Record (EHR), Interface Terminology, SNOMED, Cardiology, Machine Learning.
Abstract:
Clinical notes in Electronic Health Records (EHRs) contain large amounts of nuanced information. Healthcare professionals, e.g., clinicians, routinely review numerous EHR notes, further burdening their busy schedules. To capture the essential content of a note, they often quickly review its content, which can contribute to missing critical clinical information. Highlighting important content of EHRs enable clinicians to fast skim by reading only the highlighted words. Furthermore, effective highlighting of EHRs will support new research and interoperability. In this paper, we design a Cardiology Interface Terminology (CIT) dedicated for the application of highlighting cardiology EHRs to support their fast skimming. Once successful, Transfer Learning can be used to design an interface terminology for other specialties. In EHRs, we observe phrases of fine granularity containing SNOMED CT concepts. In our previous work, we extract such phrases from EHR notes to be considered as CIT conc
epts. This early CIT serves as training data for Machine Learning (ML) techniques, further enriching CIT and improving EHR highlighting. We describe the methodology and results of curating CIT with ML techniques. Furthermore, we introduce the coverage and breadth metrics for measuring the efficacy of highlighting EHRs, and discuss future improvements, enhancing the coverage of highlighted important content.
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