Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study
Guardat en:
| Publicat a: | Journal of Medical Internet Research vol. 26, no. 1 (2024), p. e46857 |
|---|---|
| Autor principal: | |
| Altres autors: | , , , , , , , |
| Publicat: |
Gunther Eysenbach MD MPH, Associate Professor
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2925488405 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1438-8871 | ||
| 024 | 7 | |a 10.2196/46857 |2 doi | |
| 035 | |a 2925488405 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 100 | 1 | |a Fudickar, Sebastian | |
| 245 | 1 | |a Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Intervention | ||
| 653 | |a Datasets | ||
| 653 | |a Extraction | ||
| 653 | |a Neurosurgery | ||
| 653 | |a Questionnaires | ||
| 653 | |a Rehabilitation | ||
| 653 | |a Medical referrals | ||
| 653 | |a Biopsychosocial aspects | ||
| 653 | |a Low back pain | ||
| 653 | |a Patients | ||
| 653 | |a Qualitative research | ||
| 653 | |a Machine learning | ||
| 653 | |a Electronic health records | ||
| 653 | |a Anesthesiology | ||
| 653 | |a Matrices | ||
| 653 | |a Drugs | ||
| 653 | |a Quality of life | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Back pain | ||
| 653 | |a Work status | ||
| 653 | |a Surgery | ||
| 653 | |a Psychosocial factors | ||
| 653 | |a Cohort analysis | ||
| 653 | |a Triage | ||
| 653 | |a Computerized medical records | ||
| 653 | |a Occupational status | ||
| 653 | |a Natural language processing | ||
| 653 | |a Digitization | ||
| 653 | |a Health records | ||
| 653 | |a Psychosocial functioning | ||
| 653 | |a Confusion | ||
| 653 | |a Comorbidity | ||
| 653 | |a Decision support systems | ||
| 653 | |a Accuracy | ||
| 653 | |a Demography | ||
| 653 | |a Dutch language | ||
| 653 | |a Medical records | ||
| 653 | |a Support networks | ||
| 653 | |a Letters (Correspondence) | ||
| 653 | |a Data | ||
| 653 | |a Data processing | ||
| 653 | |a Decisions | ||
| 653 | |a Treatment methods | ||
| 653 | |a Referrals | ||
| 653 | |a Medical treatment | ||
| 653 | |a Pain | ||
| 700 | 1 | |a Bantel, Carsten | |
| 700 | 1 | |a Spieker, Jannik | |
| 700 | 1 | |a Töpfer, Heinrich | |
| 700 | 1 | |a Stegeman, Patrick | |
| 700 | 1 | |a Henrica R Schiphorst Preuper | |
| 700 | 1 | |a Reneman, Michiel F | |
| 700 | 1 | |a Wolff, André P | |
| 700 | 1 | |a Soer, Remko | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 26, no. 1 (2024), p. e46857 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2925488405/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2925488405/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2925488405/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |