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Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.
Berman, Adam N; Biery, David W; Ginder, Curtis; Hulme, Olivia L; Marcusa, Daniel; Leiva, Orly; Wu, Wanda Y; Cardin, Nicholas; Hainer, Jon; Bhatt, Deepak L; Di Carli, Marcelo F; Turchin, Alexander; Blankstein, Ron.
Afiliação
  • Berman AN; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Biery DW; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Ginder C; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Hulme OL; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Marcusa D; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Leiva O; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Wu WY; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Cardin N; Division of Endocrinology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Hainer J; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Bhatt DL; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Di Carli MF; Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Turchin A; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Blankstein R; Division of Endocrinology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Clin Cardiol ; 44(9): 1296-1304, 2021 Sep.
Article em En | MEDLINE | ID: mdl-34347314
ABSTRACT

OBJECTIVE:

Accurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor-intensive and expensive, the adoption of electronic health records enables computational analysis of free-text documentation using natural language processing (NLP) tools.

HYPOTHESIS:

We sought to develop highly accurate NLP modules to assess for the presence of five key cardiovascular comorbidities in a large electronic health record system.

METHODS:

One-thousand clinical notes were randomly selected from a cardiovascular registry at Mass General Brigham. Trained physicians manually adjudicated these notes for the following five diagnostic comorbidities hypertension, dyslipidemia, diabetes, coronary artery disease, and stroke/transient ischemic attack. Using the open-source Canary NLP system, five separate NLP modules were designed based on 800 "training-set" notes and validated on 200 "test-set" notes.

RESULTS:

Across the five NLP modules, the sentence-level and note-level sensitivity, specificity, and positive predictive value was always greater than 85% and was most often greater than 90%. Accuracy tended to be highest for conditions with greater diagnostic clarity (e.g. diabetes and hypertension) and slightly lower for conditions whose greater diagnostic challenges (e.g. myocardial infarction and embolic stroke) may lead to less definitive documentation.

CONCLUSION:

We designed five open-source and highly accurate NLP modules that can be used to assess for the presence of important cardiovascular comorbidities in free-text health records. These modules have been placed in the public domain and can be used for clinical research, trial recruitment and population management at any institution as well as serve as the basis for further development of cardiovascular NLP tools.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Doenças Cardiovasculares Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Linguagem Natural / Doenças Cardiovasculares Idioma: En Ano de publicação: 2021 Tipo de documento: Article