BMC Med Inform Decis Mak - Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance.

Tópicos

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Resumo

CKGROUND: The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyon's H?pital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED) medical records. The first step consists of developing an application (UrgIndex) which automatically extracts and encodes information found in narrative reports. The purpose of the present article is to describe and evaluate this natural language processing system.METHODS: Narrative reports have to be pre-processed before utilizing the French-language medical multi-terminology indexer (ECMT) for standardized encoding. UrgIndex identifies and excludes syntagmas containing a negation and replaces non-standard terms (abbreviations, acronyms, spelling errors...). Then, the phrases are sent to the ECMT through an Internet connection. The indexer's reply, based on Extensible Markup Language, returns codes and literals corresponding to the concepts found in phrases. UrgIndex filters codes corresponding to suspected infections. Recall is defined as the number of relevant processed medical concepts divided by the number of concepts evaluated (coded manually by the medical epidemiologist). Precision is defined as the number of relevant processed concepts divided by the number of concepts proposed by UrgIndex. Recall and precision were assessed for respiratory and cutaneous syndromes.RESULTS: Evaluation of 1,674 processed medical concepts contained in 100 ED medical records (50 for respiratory syndromes and 50 for cutaneous syndromes) showed an overall recall of 85.8% (95% CI: 84.1-87.3). Recall varied from 84.5% for respiratory syndromes to 87.0% for cutaneous syndromes. The most frequent cause of lack of processing was non-recognition of the term by UrgIndex (9.7%). Overall precision was 79.1% (95% CI: 77.3-80.8). It varied from 81.4% for respiratory syndromes to 77.0% for cutaneous syndromes.CONCLUSIONS: This study demonstrates the feasibility of and interest in developing an automated method for extracting and encoding medical concepts from ED narrative reports, the first step required for the detection of potentially infectious patients at epidemic risk.

Resumo Limpo

ckground identif patient pose epidem hazard admit health facil play role prevent risk hospit acquir infect autom clinic decis support system detect suspect case base principl syndrom surveil develop univers lyon hpital de la croixrouss tool will analys structur data narrat report computer emerg depart ed medic record first step consist develop applic urgindex automat extract encod inform found narrat report purpos present articl describ evalu natur languag process systemmethod narrat report preprocess util frenchlanguag medic multiterminolog index ecmt standard encod urgindex identifi exclud syntagma contain negat replac nonstandard term abbrevi acronym spell error phrase sent ecmt internet connect index repli base extens markup languag return code liter correspond concept found phrase urgindex filter code correspond suspect infect recal defin number relev process medic concept divid number concept evalu code manual medic epidemiologist precis defin number relev process concept divid number concept propos urgindex recal precis assess respiratori cutan syndromesresult evalu process medic concept contain ed medic record respiratori syndrom cutan syndrom show overal recal ci recal vari respiratori syndrom cutan syndrom frequent caus lack process nonrecognit term urgindex overal precis ci vari respiratori syndrom cutan syndromesconclus studi demonstr feasibl interest develop autom method extract encod medic concept ed narrat report first step requir detect potenti infecti patient epidem risk

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