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

Adrian Richter, Julia Truthmann, Jean-François Chenot and Carsten Oliver Schmidt
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Adrian Richter: Department SHIP-KEF, Institute for Community Medicine, Greifswald University Medical Center, Walther Rathenau Str. 48, 17475 Greifswald, Germany
Julia Truthmann: Department of Family Medicine, Institute for Community Medicine, Fleischmannstr. 42, 17475 Greifswald, Germany
Jean-François Chenot: Department of Family Medicine, Institute for Community Medicine, Fleischmannstr. 42, 17475 Greifswald, Germany
Carsten Oliver Schmidt: Department SHIP-KEF, Institute for Community Medicine, Greifswald University Medical Center, Walther Rathenau Str. 48, 17475 Greifswald, Germany

IJERPH, 2021, vol. 18, issue 22, 1-14

Abstract: (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.

Keywords: record linkage; machine learning; calibration; best subset selection; low back pain (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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