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

Randa Al-Kharabsheh, Muayyad Ahmad

Abstract

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.

Introduction

Unplanned readmissions, defined as a return to the hospital within 30 days of discharge from general care or 90 days for critical care for reasons related to the index admission, are often linked to urgent health crises and lead to increased morbidity, mortality, and financial costs [1,2]. The most common window for unplanned readmissions that aims for quality improvement and benchmarking between health institutions was 30 days for both floors and critical care units.

Methods

A retrospective analysis was conducted on data from 2,795 adult patients admitted for neurological/neurosurgical reasons between November 2022 and August 2024. Patients’ diagnosis was categorized and analyzed based on the International Classification of Diseases and Related Health Problems (ICD), Tenth Revision.

Results

Among 2,795 patients, 249 (8.9%) experienced an unplanned readmission. As presented in S2 Table, among those who had floor index admissions; a total of 211 (8.5%) patients experienced 30-day unplanned readmissions, while among those who had critical care index admissions; a total of 38 (11.8%) patients experienced 90-day unplanned readmissions.

Discussion

This study presents one of the early studies that used an AI-driven, large-scale analysis of unplanned readmissions in a Middle Eastern neurological cohort, yielding critical insights with both regional and global relevance.

Acknowledgments

We extend our sincere gratitude to the Jordanian Royal Medical Services, the Electronic Health Solutions, and the Health Data Analytics for providing us with the patients’ medical data.

Citation: Al-Kharabsheh R, Ahmad M (2026) Predicting unplanned readmissions in neurological patients: A large-scale AI-driven analysis using CHAID decision trees. PLoS One 21(10): e0360077. https://doi.org/10.1371/journal.pone.0360077

Editor: Yangming Ou, Harvard Medical School, UNITED STATES OF AMERICA

Received: February 14, 2026; Accepted: September 22, 2026; Published: October 9, 2026

Copyright: © 2026 Al-Kharabsheh, Ahmad. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because it were used under license, and restrictions were applied to public availability. Data are available from the Jordanian Royal Medical Services Institutional Data Access / Ethics Committee (FAX: 962-6-5347485 TEL: 5804804 EXT: 4341/ 4373 P.O. BOX: 122 Dahyat Al-Amir Rashid, Amman 11831 Jordan) for researchers who meet the criteria for access to confidential data.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.