Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.

Journal: Expert review of pharmacoeconomics & outcomes research
Published Date: Mar 5, 2024

Abstract

INTRODUCTION: Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking.

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.
  • 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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