A systematic literature review of automated clinical coding and classification systems
Author(s) -
Mary H Stanfill,
Margaret Williams,
Susan H. Fenton,
Robert A. Jenders,
William Hersh
Publication year - 2010
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1136/jamia.2009.001024
Subject(s) - computer science , coding (social sciences) , systematic review , data science , variety (cybernetics) , medical classification , medline , classification scheme , artificial intelligence , natural language processing , information retrieval , machine learning , medicine , pathology , statistics , mathematics , political science , law
Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome.
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