Natural language processing of clinical notes for identification of critical limb ischemia
Abstract Background Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHRs) is challenging due to absence of a single definitive International Classification of Diseases (ICD-9 or ICD-10) code for CLI. Methods and results In this study, we extend a previously validated natural language processing (NLP) algorithm for PAD identification to develop and validate a subphenotyping NLP algorithm (CLI-NLP) for identification of CLI cases from clinical notes. We compared performance of the CLI-NLP algorithm with CLI-related ICD-9 billing codes. The gold standard for validation was human abstraction of clinical notes from EHRs. Compared to billing codes the CLI-NLP algorithm had higher positive predictive value (PPV) (CLI-NLP 96%, billing codes 67%, p Conclusions The CLI-NLP algorithm for identification of CLI from narrative clinical notes in an EHR had excellent PPV and has potential for translation to patient care as it will enable automated identification of CLI cases for quality projects, clinical decision support tools and support a learning healthcare system. Highlights Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease. Automated identification of cases is challenging due to absence of a single International Classification of Diseases code for CLI. A natural language processing (NLP)-based algorithm for ascertainment of CLI from narrative clinical notes was developed. The CLI-NLP algorithm for identification of CLI had excellent positive predictive value with potential for translation to patient care.
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