Comparing natural language processing tools to extract medical problems from narrative text.

Stéphane M Meystre; Peter J Haug

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PubMed · 16779095

Comparing natural language processing tools to extract medical problems from narrative text.

Stéphane M Meystre·Peter J Haug

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Abstract

To help maintain a complete, accurate and timely Problem List, we are developing a system to automatically retrieve medical problems from free-text documents. This system uses Natural Language Processing to analyze all electronic narrative text documents in a patient's record. Here we evaluate and compare 3 different applications of NLP technology in our system: the first using MMTx (MetaMap Transfer) with a negation detection algorithm (NegEx), the second using an alpha version of a locally developed NLP application called MPLUS2, and the third using keyword searching. They were adapted and trained to extract medical problems from a set of 80 problems of diagnosis type. The version using MMTx and NegEx was improved by adding some disambiguation and modifying the negation detection algorithm, and these modifications significantly improved recall and precision. The different versions of the NLP module were compared, and showed the following recall / precision results: standard MMTx with NegEx version 0.775 / 0.398; improved MMTx with NegEx version 0.892 / 0.753; MPLUS2 version 0.693 / 0.402; and keyword searching version 0.575 / 0.807. Average results for the reviewers were a recall of 0.788 and a precision of 0.912.

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Algorithms Bayes Theorem Humans Information Storage and Retrieval Medical Records Systems, Computerized Medical Records, Problem-Oriented Natural Language Processing Neural Networks, Computer Unified Medical Language System Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Stéphane M Meystre, Peter J Haug. 2005. Comparing natural language processing tools to extract medical problems from narrative text.. https://pubmed.ncbi.nlm.nih.gov/16779095/

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