An interpretable natural language processing system for written medical examination assessment.

Journal: Journal of biomedical informatics

Published Date: Oct 1, 2019

Abstract

OBJECTIVE: The assessment of written medical examinations is a tedious and expensive process, requiring significant amounts of time from medical experts. Our objective was to develop a natural language processing (NLP) system that can expedite the assessment of unstructured answers in medical examinations by automatically identifying relevant concepts in the examinee responses.

Authors

Abeed Sarker

Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Ari Z Klein

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Janet Mee

National Board of Medical Examiners, Philadelphia, PA, USA.
Polina Harik
Graciela Gonzalez-Hernandez

Health Language Processing Center, Institute for Biomedical Informatics at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Keywords

Algorithms Clinical Competence Data Collection Data Curation Education, Medical Educational Measurement Fuzzy Logic Humans Licensure, Medical Medical Records Natural Language Processing Pattern Recognition, Automated Reproducibility of Results Schools, Medical Software Unified Medical Language System

External Resources

View on PubMed Access via DOI PubMed (31421211)

An interpretable natural language processing system for written medical examination assessment.

Abstract

Authors

Keywords

External Resources

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