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Automated Encoding of Clinical Documents Based on Natural Language Processing

Affiliation of the authors: Department of Biomedical Informatics, College of Physicians and Surgeons, Columbia University, New York, NY

Correspondence and reprints: Carol Friedman, PhD, Department of Biomedical Informatics, Columbia University, 622 West 168 Street, VC-5, New York, NY 10032; e-mail: <friedman{at}dbmi.columbia.edu>.

Received for publication: 02/04/04; accepted for publication: 04/13/04.

Objective: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method.

Methods: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts.

Results: Recall of the system for UMLS coding of all terms was .77 (95% CI .72–.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79–.87). Recall of the system for extracting all terms was .84 (.81–.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87–.91), and precision of the experts ranged from .61 to .91.

Conclusion: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.

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