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Predicting Depression among Patients with Diabetes Using Longitudinal Data. A Multilevel Regression Model.

H Jin1, S Wu, I Vidyanti

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|November 19, 2015
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Summary

A new multilevel regression model accurately predicts depression in diabetes patients using longitudinal data. This tool helps providers proactively identify and manage depressive symptoms, improving patient outcomes.

Keywords:
Depressioncomorbiditydiabetes mellitusmachine learningmultilevel regression

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Area of Science:

  • Healthcare Analytics
  • Medical Informatics
  • Big Data in Medicine

Background:

  • Depression is prevalent and often undiagnosed in diabetes patients, significantly impacting health outcomes, healthcare utilization, and costs.
  • Predicting depression in this population is crucial for proactive assessment and intervention, potentially reducing suicide risk.

Purpose of the Study:

  • To develop a generalized multilevel regression model for predicting depression severity and major depression in diabetes patients.
  • To utilize longitudinal data from a large clinical trial for model development.

Main Methods:

  • A 2-level Poisson regression model was developed using selected predictors from 29 candidate factors.
  • Depression severity was measured using the Patient Health Questionnaire (PHQ-9) score.
  • Model accuracy was evaluated using Root-Mean-Square Error (RMSE) and classification ability for major depression.

Main Results:

  • The final model included two time-invariant and 10 time-varying predictors.
  • Subject-specific predictions, incorporating historical patient data, demonstrated higher accuracy (RMSE ~4, ROC AUC ~0.9) than population-average predictions.
  • Updating the model with newly obtained patient records potentially enhances predictive accuracy and classification.

Conclusions:

  • A generalized multilevel regression model effectively predicts depression in diabetes patients.
  • Longitudinal patient data and subject-specific predictions yield high predictive ability for depression assessment.