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

Journal Article (Journal Article;Review;Systematic Review)

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.

Full Text

Duke Authors

Cited Authors

  • Reading Turchioe, M; Volodarskiy, A; Pathak, J; Wright, DN; Tcheng, JE; Slotwiner, D

Published Date

  • May 25, 2022

Published In

Volume / Issue

  • 108 / 12

Start / End Page

  • 909 - 916

PubMed ID

  • 34711662

Pubmed Central ID

  • PMC9046466

Electronic International Standard Serial Number (EISSN)

  • 1468-201X

Digital Object Identifier (DOI)

  • 10.1136/heartjnl-2021-319769


  • eng

Conference Location

  • England