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Systematic review of current natural language processing methods and applications in cardiology.

Authors
  • Reading Turchioe, Meghan1
  • Volodarskiy, Alexander2
  • Pathak, Jyotishman3
  • Wright, Drew N4
  • Tcheng, James Enlou5
  • Slotwiner, David3, 2
  • 1 Department of Population Health Sciences, Division of Health Informatics, Weill Cornell Medicine, New York, New York, USA [email protected]
  • 2 Department of Medicine, Division of Cardiology, NewYork-Presbyterian Hospital, New York, New York, USA.
  • 3 Department of Population Health Sciences, Division of Health Informatics, Weill Cornell Medicine, New York, New York, USA.
  • 4 Samuel J. Wood Library & C.V. Starr Biomedical Information Center, Weill Cornell Medical College, New York, New York, USA.
  • 5 Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
Type
Published Article
Journal
Heart
Publisher
BMJ
Publication Date
May 25, 2022
Volume
108
Issue
12
Pages
909–916
Identifiers
DOI: 10.1136/heartjnl-2021-319769
PMID: 34711662
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

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