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Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees

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  • Randa Al-Kharabsheh
  • Muayyad Ahmad

Abstract

Background: Unplanned readmissions in neurology signify care gaps, increasing costs, and morbidity. This study aimed to develop and internally validate an AI model to predict unplanned readmissions among neurological patients in Jordan. Method and materials: We conducted a retrospective analysis of 2,795 adult neurology admissions (2022–2024). Patients’ diagnosis was grouped and analyzed based on the International Classification of Diseases and Related Health Problems, Tenth Revision. Nine prediction models were compared using Electronic Health Record (EHR) data. The Chi-square Automatic Interaction Detection (CHAID) decision tree was selected for optimal performance and interpretability. Results: The unplanned readmission rate was 8.9%, highest in the neoplasm group (24%). The CHAID model demonstrated modest discriminative ability (AUC = 0.671). For risk stratification, it achieved a balanced accuracy of 0.63, sensitivity of 0.52, and specificity of 0.74. Key predictors were Charlson Comorbidity Index (CCI ≥ 5), age, geographic region, length of stay, and sex. Risk-stratification metrics were calculated descriptively from the complete cohort, whereas independent model validation was based on the held-out test-set AUC. Subgroup analysis revealed distinct risk profiles: vascular patients with high CCI (p

Suggested Citation

  • Randa Al-Kharabsheh & Muayyad Ahmad, 2026. "Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees," PLOS ONE, Public Library of Science, vol. 21(10), pages 1-11, October.
    • Handle: RePEc:plo:pone00:0360077
    DOI: 10.1371/journal.pone.0360077
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