Natural Language Processing of Referral Letters for Machine Learning-Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study

Decision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appear...

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Bibliographic Details
Published in:	Journal of medical Internet research Vol. 26; no. 10070; p. e46857
Main Authors:	Fudickar, Sebastian, Bantel, Carsten, Spieker, Jannik, Töpfer, Heinrich, Stegeman, Patrick, Schiphorst Preuper, Henrica R, Reneman, Michiel F, Wolff, André P, Soer, Remko
Format:	Journal Article
Language:	English
Published:	Canada Journal of Medical Internet Research 30-01-2024 Gunther Eysenbach MD MPH, Associate Professor JMIR Publications
Subjects:	Accuracy Anesthesiology Artificial intelligence Back pain Backache Biopsychosocial aspects Cohort analysis Comorbidity Computational linguistics Computerized medical records Confusion Datasets Decision support systems Digitization Drugs Electronic health records Evidence-based medicine Extraction Health records Humans Intervention Language processing Low back pain Low Back Pain - diagnosis Low Back Pain - therapy Machine Learning Matrices Medical records Medical referrals Natural language interfaces Natural Language Processing Neurosurgery Occupational status Original Paper Patients Psychosocial factors Psychosocial functioning Qualitative research Quality of Life Questionnaires Rehabilitation Retrospective Studies Surgery Triage Work status clinical application pain questionnaire natural language processing low back pain support system back decision support qualitative data machine learning neural network artificial intelligence NLP triaging decision-making psychosocial LBP patient records quality of life
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Summary:	Decision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients' goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment. The objective of this study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP. A retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models' accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters. Data from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results. This study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1438-8871 1439-4456 1438-8871
DOI:	10.2196/46857