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Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach

  • (1) Background: Predicting chronic low back pain (LBP) is of clinical and economic interest as LBP leads to disabilities and health service utilization. This study aims to build a competitive and interpretable prediction model; (2) Methods: We used clinical and claims data of 3837 participants of a population-based cohort study to predict future LBP consultations (ICD-10: M40.XX-M54.XX). Best subset selection (BSS) was applied in repeated random samples of training data (75% of data); scoring rules were used to identify the best subset of predictors. The rediction accuracy of BSS was compared to randomforest and support vector machines (SVM) in the validation data (25% of data); (3) Results: The best subset comprised 16 out of 32 predictors. Previous occurrence of LBP increased the odds for future LBP consultations (odds ratio (OR) 6.91 [5.05; 9.45]), while concomitant diseases reduced the odds (1 vs. 0, OR: 0.74 [0.57; 0.98], >1 vs. 0: 0.37 [0.21; 0.67]). The area-under-curve (AUC) of BSS was acceptable (0.78 [0.74; 0.82]) and comparable with SVM (0.78 [0.74; 0.82]) and randomforest (0.79 [0.75; 0.83]); (4) Conclusions: Regarding prediction accuracy, BSS has been considered competitive with established machine-learning approaches. Nonetheless, considerable misclassification is inherent and further refinements are required to improve predictions.

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Metadaten
Author: Adrian Richter, Julia Truthmann, Jean-François Chenot, Carsten Oliver Schmidt
URN:urn:nbn:de:gbv:9-opus-57685
DOI:https://doi.org/https://doi.org/10.3390/ijerph182212013
ISSN:1660-4601
Parent Title (English):International Journal of Environmental Research and Public Health
Publisher:MDPI
Editor: Madhan Balasubramanian, Benjumin Hsu, Jimmy T. Efird
Document Type:Article
Language:English
Date of first Publication:2021/11/16
Release Date:2022/06/14
Tag:best subset selection; calibration; low back pain; machine learning; record linkage
GND Keyword:-
Volume:18
Issue:22
Article Number:12013
Faculties:Universitätsmedizin / Institut für Community Medicine
Collections:Artikel aus DFG-gefördertem Publikationsfonds
Licence (German):License LogoCreative Commons - Namensnennung