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Related Experiment Video

Updated: Oct 12, 2025

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

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Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data-An

Adrian Richter1, Julia Truthmann2, Jean-François Chenot2

  • 1Department SHIP-KEF, Institute for Community Medicine, Greifswald University Medical Center, Walther Rathenau Str. 48, 17475 Greifswald, Germany.

International Journal of Environmental Research and Public Health
|November 27, 2021
PubMed
Summary

Predicting chronic low back pain (LBP) is crucial. A best subset selection model showed competitive accuracy against machine learning, identifying previous LBP as a key predictor.

Keywords:
best subset selectioncalibrationlow back painmachine learningrecord linkage

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Area of Science:

  • Epidemiology
  • Health Informatics

Background:

  • Chronic low back pain (LBP) presents significant clinical and economic challenges.
  • Accurate prediction of LBP is essential for managing disabilities and healthcare utilization.

Purpose of the Study:

  • To develop a competitive and interpretable prediction model for future low back pain consultations.
  • To compare the performance of best subset selection (BSS) with machine learning algorithms.

Main Methods:

  • Utilized clinical and claims data from 3837 participants in a population-based cohort study.
  • Applied best subset selection (BSS) on training data (75%) to identify optimal predictors.
  • Validated model performance against random forest and support vector machines (SVM) on a separate validation set (25%).

Main Results:

  • The optimal subset included 16 out of 32 predictors.
  • Previous LBP history significantly increased odds of future LBP consultations (OR 6.91).
  • Concomitant diseases decreased the odds of future LBP consultations.
  • BSS achieved an acceptable Area Under the Curve (AUC) of 0.78, comparable to SVM (0.78) and random forest (0.79).

Conclusions:

  • Best subset selection (BSS) demonstrates competitive prediction accuracy against advanced machine learning methods for LBP.
  • Despite comparable performance, inherent misclassification necessitates further model refinement for improved low back pain predictions.