Natural language processing: using artificial intelligence to understand human language in orthopedics

  • James A. Pruneski
  • , Ayoosh Pareek
  • , Benedict U. Nwachukwu
  • , R. Kyle Martin
  • , Bryan T. Kelly
  • , Jón Karlsson
  • , Andrew D. Pearle
  • , Ata M. Kiapour
  • , Riley J. Williams

Research output: Contribution to journalReview articlepeer-review

34 Scopus citations

Abstract

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

Original languageEnglish (US)
Pages (from-to)1203-1211
Number of pages9
JournalKnee Surgery, Sports Traumatology, Arthroscopy
Volume31
Issue number4
DOIs
StatePublished - Apr 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Natural language processing
  • Predictive analytics

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