Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not specified in abstract

• Primary Outcome: Unplanned all-cause hospital readmission within 30 days

• Nnt Or Sensitivity: Improved discrimination with synthetic data augmentation compared to original data models (specific metrics not provided)

• Confidence Interval: Not reported

Clinical Application

Requires electronic health record infrastructure and machine learning capabilities. Implementation complexity moderate to high Relevant for Australian hospitals with EMR systems. Key predictors (medication adherence, care gaps) align with Australian chronic disease management priorities. Would need validation in Australian healthcare context given different care delivery models Adult patients with COPD, heart failure, or type 2 diabetes in hospital settings

Abstract

OBJECTIVES: To develop and evaluate an explainable machine learning framework enhanced with synthetic data generation to predict unplanned 30-day hospital readmissions among patients with chronic obstructive pulmonary disease (COPD), heart failure (HF) and type 2 diabetes mellitus (T2DM), and to identify key clinical and social predictors of readmission. DESIGN: A retrospective cohort study using electronic health record data incorporating both structured variables and information extracted from unstructured clinical notes. Synthetic data were generated using advanced resampling and deep learning-based techniques to address outcome imbalance and improve model training. SETTING: Intensive care unit and general ward admissions at a single tertiary academic medical centre included in the MIMIC-IV (Medical Information Mart for Intensive Care IV) database. PARTICIPANTS: Adult patients (≥18 years) were admitted with a primary diagnosis of COPD (n=14 050), HF (n=7097) or T2DM (n=12 735) between 2008 and 2019, with complete 30-day follow-up and no in-hospital mortality during the index admission. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was unplanned all-cause hospital readmission within 30-days of discharge. Predictors were drawn from six domains, including demographics, comorbidities, clinical acuity, therapies, behavioural factors and care continuity. Predictive performance was evaluated using multiple machine learning methods and fivefold cross-validation, with model interpretability assessed using established goal and local explanation approaches. RESULTS: Ensemble-based machine learning models demonstrated the strongest predictive performance across all three disease cohorts. Key predictors of readmission included higher illness severity, greater comorbidity burden, medication non-adherence, gaps in preventive care and limited social support. Models incorporating synthetic data augmentation showed improved discrimination compared with models trained on original data alone. CONCLUSIONS: An explainable synthetic-data driven framework incorporating clinical, behavioural and social data can support prediction of 30-day readmissions among patients with common chronic conditions using routinely available electronic health record data.

References

1. 1.Brothers, T., Adhikari, K., Ramazani, M., Imtiaz, A., & Al-Mamun, M. (2026). Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study. BMJ Open, Article 108273. https://doi.org/10.1136/bmjopen-2025-108273