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Identifying Acute Low Back Pain Episodes in Primary Care Practice from Clinical Notes: Observational Study



Details

  • Personal Author:
  • Description:
    Background: Acute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options. Objective: The objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes. Methods: We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels. Results: ConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet's results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity. Conclusions: This study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • ISSN:
    2291-9694
  • Document Type:
    Text
  • Funding:
    Grant
  • Genre:
    Journal Article
  • Place as Subject:
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • Topic:
    Workplace Safety & Health
  • Location:
    North America
  • Volume:
    8
  • Issue:
    2
  • NIOSHTIC Number:
    nn:20059099
  • Citation:
    JMIR Med Inform 2020 Feb; 8(2):e16878
  • Contact Point Address:
    Ismail Nabeel, MD, MPH, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 East 102nd Street, Box 1043, New York, NY, 10029, United States
  • Email:
    ismail.nabeel@icahn.mssm.edu
  • Federal Fiscal Year:
    2020
  • Performing Organization:
    Mount Sinai School of Medicine
  • Peer Reviewed:
    False
  • Start Date:
    20050701
  • Source Full Name:
    JMIR Medical Informatics
  • End Date:
    20270630
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:38ca61d9da8198a0ad8be7161a54ba7a18fcca45652e33f927a1d6fb2a7a3f68f9d0a503fe616d37e21df3ff05c3131c03a8dd897bae2c32f5c1d59d47c2f5ed
  • Download URL:
  • File Type:
    Filetype[PDF - 561.23 KB ]
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