Int J Med Inform - Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.

Tópicos

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Resumo

JECTIVE: Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.DESIGN: We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown.MEASUREMENTS: Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling.RESULTS: Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932.CONCLUSION: Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.

Resumo Limpo

jectiv earli detect heart failur hf mitig enorm individu societ burden diseas clinic detect base part recognit multipl sign symptom compris framingham hf diagnost criteria typic document necessarili synthes primari care physician well specif diagnost studi done develop natur languag process nlp procedur identifi framingham hf sign symptom among primari care patient use electron health record ehr clinic note prelud pattern analysi clinic decis support earli detect hfdesign develop hybrid nlp pipelin perform two level analysi criteria mention level rulebas nlp system construct annot affirm negat mention framingham criteria encount level construct system label encount accord whether framingham criterion assert deni unknownmeasur precis recal fscore use perform metric criteria mention extract encount labelingresult criteria mention extract achiev precis recal fscore encount label achiev fscore conclus system accur identifi label affirm denial framingham diagnost criteria primari care clinic note may help attempt improv earli detect hf adapt tool develop methodolog can repeat new problem set

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