Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.

Journal: Artificial intelligence in medicine
Published Date: Nov 1, 2023

Abstract

OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

Authors

  • Jin-Ah Sim
    Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  • Xiaolei Huang
    Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA.
  • Madeline R Horan
    Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • Christopher M Stewart
    Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States.
  • Leslie L Robison
    Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • Melissa M Hudson
    Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • Justin N Baker
    Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • I-Chan Huang
    Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.

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