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Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations: A Population-Based

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Summary
This summary is machine-generated.

Artificial intelligence effectively predicts potentially preventable hospitalizations. Municipality health services show a preventive effect, particularly for older adults, reducing hospitalization risks.

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Area of Science:

  • Healthcare Analytics
  • Predictive Modeling
  • Public Health

Background:

  • Growing elderly population and strained healthcare resources necessitate reducing hospitalizations.
  • Potentially preventable hospitalizations are a key focus for healthcare policy.
  • Need for advanced tools to identify at-risk individuals for proactive interventions.

Purpose of the Study:

  • To develop an artificial intelligence (AI) model for predicting potentially preventable hospitalizations.
  • To utilize explainable AI (XAI) to identify key predictors and their interactions.
  • To inform targeted public health strategies and resource allocation.

Main Methods:

  • Utilized the Danish CROSS-TRACKS cohort (2016-2017) for prediction.
  • Employed extreme gradient boosting for predictive modeling.
  • Applied Shapley additive explanations (SHAP) for model interpretability.

Main Results:

  • Achieved an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.789.
  • Identified key predictors: age, obstructive airway disease medications, antibiotics, and municipality service use.
  • Discovered a protective interaction: older adults (75+) using municipality services had lower hospitalization risk.

Conclusions:

  • AI models are effective for predicting potentially preventable hospitalizations.
  • Municipality-based health services demonstrate a significant preventive impact.
  • Findings support integrating community-level services into preventative healthcare strategies.