AMIA Annu Symp Proc - Natural language processing to extract follow-up provider information from hospital discharge summaries.

Tópicos

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Resumo

JECTIVE: We evaluate the performance of a Natural Language Processing (NLP) application designed to extract follow-up provider information from free-text discharge summaries at two hospitals.EVALUATION: We compare performance by the NLP application, called the Regenstrief EXtracion tool (REX), to performance by three physician reviewers at extracting follow-up provider names, phone/fax numbers and location information. Precision, recall, and F-measures are reported, with 95% CI for pairwise comparisons.RESULTS: Of 556 summaries with follow-up information, REX performed as follows in precision, recall, F-measure respectively: Provider Name 0.96, 0.92, 0.94; Phone/Fax 0.99, 0.92, 0.96; Location 0.83, 0.82, 0.82. REX was as good as all physician-reviewers in identifying follow-up provider names and phone/fax numbers, and slightly inferior to two physicians at identifying location information. REX took about four seconds (vs. 3-5 minutes for physician-reviewers) to extract follow-up information.CONCLUSION: A NLP program had physician-like performance at extracting provider follow-up information from discharge summaries.

Resumo Limpo

jectiv evalu perform natur languag process nlp applic design extract followup provid inform freetext discharg summari two hospitalsevalu compar perform nlp applic call regenstrief extracion tool rex perform three physician review extract followup provid name phonefax number locat inform precis recal fmeasur report ci pairwis comparisonsresult summari followup inform rex perform follow precis recal fmeasur respect provid name phonefax locat rex good physicianreview identifi followup provid name phonefax number slight inferior two physician identifi locat inform rex took four second vs minut physicianreview extract followup informationconclus nlp program physicianlik perform extract provid followup inform discharg summari

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