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Systematic review of current natural language processing methods and applications in cardiology
  1. Meghan Reading Turchioe1,
  2. Alexander Volodarskiy2,
  3. Jyotishman Pathak1,
  4. Drew N Wright3,
  5. James Enlou Tcheng4,
  6. David Slotwiner1,2
  1. 1Department of Population Health Sciences, Division of Health Informatics, Weill Cornell Medicine, New York, New York, USA
  2. 2Department of Medicine, Division of Cardiology, NewYork-Presbyterian Hospital, New York, New York, USA
  3. 3Samuel J. Wood Library & C.V. Starr Biomedical Information Center, Weill Cornell Medical College, New York, New York, USA
  4. 4Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
  1. Correspondence to Dr Meghan Reading Turchioe, Department of Population Health Sciences, Division of Health Informatics, Weill Cornell Medicine, New York, New York, USA; mjr2011{at}med.cornell.edu

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.

  • electronic health records
  • heart failure
  • electrophysiology
  • coronary artery disease

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Footnotes

  • Twitter @MeghanTurchioe

  • Contributors MRT and DS conceptualised the idea for the review. MRT, AV and DS searched and screened eligible studies, extracted data and conducted quality appraisal. DNW advised on the search strategies, eligibility criteria and quality appraisal methods. MRT drafted the initial manuscript including tables and figures. AV, JP, DNW, JET and DS provided critical feedback on the manuscript.

  • Funding This work was supported by a National Institute of Nursing Research career development award (K99NR019124; PI: Reading Turchioe).

  • Competing interests MRT and JP are affiliated with Iris OB Health Inc., New York, a startup company focused on postpartum depression, and have equity ownership. MRT is a consultant for Boston Scientific Corporation.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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