TY  - JOUR
AU  - Fudickar, Sebastian
AU  - Bantel, Carsten
AU  - Spieker, Jannik
AU  - Töpfer, Heinrich
AU  - Stegeman, Patrick
AU  - Schiphorst Preuper, Henrica R
AU  - Reneman, Michiel F
AU  - Wolff, André P
AU  - Soer, Remko
PY  - 2024
DA  - 2024/1/30
TI  - Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study
JO  - J Med Internet Res
SP  - e46857
VL  - 26
KW  - decision support
KW  - triaging
KW  - NLP
KW  - natural language processing
KW  - neural network
KW  - LBP
KW  - low back pain
KW  - back
KW  - pain
KW  - decision-making
KW  - machine learning
KW  - artificial intelligence
KW  - clinical application
KW  - patient records
KW  - qualitative data
KW  - support system
KW  - questionnaire
KW  - quality of life
KW  - psychosocial
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e46857
UR  - https://doi.org/10.2196/46857
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38289669
DO  - 10.2196/46857
ID  - info:doi/10.2196/46857
ER  -