ArticleBMJ open2025
Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Tackling Imbalanced Data in Chronic Obstructive Pulmonary Disease Diagnosis: An Ensemble Learning Approach with Synthetic Data Generation.Bioengineering (Basel, Switzerland) · 2026Article
- Leveraging a machine learning model to predict hospital readmission risk: integrating clinical and social determinants of health data.Frontiers in public health · 2026Article
- Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients.Scientific reports · 2025Article
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8 authors.
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Abstract
objectivesUnplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Given that the 14-day readmission rate is considered a healthcare quality indicator, this study is the first to develop survival machine learning (ML) models using emergency department (ED) data to predict 14-day readmission risk following pneumonia-related admissions.
designA retrospective multicentre cohort study.
settingThis study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals.
participants11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: The dataset was randomly split into training (80%), validation (10%) and independent test (10%) sets. Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. Four survival ML models-CoxNet, Survival Tree, Gradient Boosting Survival Analysis and Random Survival Forest-were developed and compared on the validation set. The best performance model was tested on the independent test set.
resultsThe RSF model outperformed the other models. Validation on an independent test set confirmed the model's robustness (C-index=0.710; AUC=0.693). The most important predictive features included creatinine levels, age, haematocrit levels, Charlson Comorbidity Index scores, and haemoglobin levels, with their predictive value changing over time.
conclusionsThe RSF model effectively predicts 14-day readmission risk among pneumonia patients. The ED data-based model allows clinicians to estimate readmission risk before ward admission or discharge from the ED, enabling timely interventions. Accurately predicting short-term readmission risk might also further support physicians in designing the optimal healthcare programme and controlling individual medical status to prevent readmissions.
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