TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Richter, Adrian
A1  - Truthmann, Julia
A1  - Chenot, Jean-François
A1  - Schmidt, Carsten Oliver
ED  - Balasubramanian, Madhan
ED  - Hsu, Benjumin
ED  - Efird, Jimmy T.
T1  - Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data—An Interpretable Machine Learning Approach
JF  - International Journal of Environmental Research and Public Health
N2  - (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.
KW  - -
KW  - record linkage
KW  - machine learning
KW  - calibration
KW  - best subset selection
KW  - low back pain
UN  - https://nbn-resolving.org/urn:nbn:de:gbv:9-opus-57685
SN  - 1660-4601
SS  - 1660-4601
U6  - https://doi.org/https://doi.org/10.3390/ijerph182212013
DO  - https://doi.org/https://doi.org/10.3390/ijerph182212013
VL  - 18
IS  - 22
PB  - MDPI
ER  -