ArticlePloS one2026
Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundUnplanned 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.
resultsThe 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 < .001, χ2 = 66.368), young females of whom 37.3% had neoplasms (p = .037, χ2 = 4.367), and functional disorder patients from northern regions (p = .009, χ2 = 8.897).
conclusionThis is one of the first studies from the Middle East to demonstrate the potential of the CHAID model for risk stratification in neurology readmissions. The model offers an interpretable framework to identify high-risk patient subgroups but requires external validation before clinical application. Its poor-to-fair discriminative performance (AUC = 0.671) supports population-level risk stratification rather than individual-level prediction.
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