Abstract
The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved.
Highlights
Valuable clinical data is held in electronic health records in the form of unstructured text (Esteva et al, 2019)
This study explores the utility of MetaMap for the high throughput phenotyping of neurological patients
Examples of extraneous concepts include d cognition, eye, ocular, death, short-term memory, diagnosed, patient outcome, problem, and many others. These extraneous concepts appear in the Unified Medical Language System Metathesaurus (UMLS) Metathesaurus but are not in the limited ontology of 1531 neurological concepts used for phenotyping (Hier & Brint 2020)
Summary
Valuable clinical data is held in electronic health records in the form of unstructured text (Esteva et al, 2019). One approach to making unstructured clinical data computable has been called deep phenotyping (Kohler et al, 2019). Deep phenotyping is the conversion of the signs and symptoms of a patient to concepts from a target ontology (Kohler et al 2014; Robinson 2012). Phenotyping can be done by manual methods, high throughput methods are needed to phenotype large numbers of patients in precision medicine (Robinson, 2012). High throughput phenotyping depends on the rapid extraction of signs and symptoms from large text sources. The text mining of electronic health records relies on methods of named entity recognition (NER) derived from natural language processing (Marrero, Urbano, Sanchez-Cuadrado, Morato, & Gomez- Berbıs, 2013; Kimia, Savova, Landschaft, & Harper, 2015). Fu et al (2020) defined concept extraction as a two-stage process in which medical concepts are first identified in text and mapped to a concept in a disease ontology
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