Observational studyBMC health services research2026
Predicting unplanned 30-day hospital readmissions in patients with urogenital system disorders: a retrospective EMR-based ensemble learning study in Taiwan.
Observational study in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundHospital readmissions are a major burden for patients, families, and healthcare systems. Artificial intelligence (AI) and electronic medical records (EMR) offer new opportunities to improve clinical decision support by identifying patients at high risk of unplanned readmission. However, accurate prediction remains challenging in urogenital system disorder populations.
methodsWe conducted a retrospective observational study using EMR data from Taichung Tzu Chi Hospital (TTCH), Taiwan, between December 2018 and January 2022. Adult inpatients diagnosed with urogenital system disorders (ICD-10 N00-N99) were included, while planned readmissions were excluded. A total of 112 EMR features were extracted per patient. Data preprocessing and class balancing were applied. Random forest-based variable importance measures were employed for automatic feature selection, resulting in the identification of 34 key predictors. Seven machine learning models, including deep learning and ensemble approaches, were developed and evaluated using a 79.5% training and 20.5% testing split.
resultsThe final dataset included 7,413 cases, with 321 unplanned 30-day readmissions. Among all models, the ensemble model achieved the best predictive performance, attaining the highest area under the receiver operating characteristic curve (AUC) of 0.827, and outperforming deep neural network, CatBoost, XGBoost, random forest, standard logistic regression, and logistic regression using only the LACE score. The selected 34 predictors contributed substantially to readmission risk identification.
conclusionsOur AI-based ensemble framework accurately predicts unplanned 30-day hospital readmissions among patients with urogenital disorders. This approach may support early risk stratification, targeted interventions, and improved clinical decision-making, contributing to the development of smart hospitals and preventive healthcare systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.