Predicting Unplanned Hospital Readmissions in People with Multiple Long-Term Conditions

The prevalence of multiple long-term conditions (MLTCs) is associated with increased healthcare utilisation and an elevated risk of unplanned 30-day hospital readmission. Existing prediction tools predominantly focus on single-disease cohorts and fail to capture the clinical heterogeneity, polypharmacy, and care complexity characteristic of MLTC populations. Using data from 99,207 UK Biobank (UKBB) participants with MLTCs ( ≥ 2 long-term conditions), we developed Self-HR, a two-stage self-supervised learning framework that learns transferable patient representations from longitudinal clinical data encompassing hospital admission diagnoses, primary care prescriptions, long-term condition histories, and demographic factors. Self-HR achieved an AUROC of 0.92 and AUPRC of 0.75 in the UKBB discovery cohort, outperforming all supervised baselines — including XGBoost, Random Forest, and fully supervised neural networks — across both overall and minority-class metrics. Performance was sustained upon external validation in 79,224 multimorbid individuals from the Clinical Practice Research Datalink (CPRD; AUROC 0.86, F1 score 0.67 for the readmitted class). Self-HR demonstrated superior robustness to partial outcome labelling and class imbalance, maintaining an F1 score of 0.62 for readmitted patients when trained on only 50% labelled data, compared with 0.28 for the best supervised comparator. Ablation analyses identified incident admission diagnoses as the strongest predictive feature, followed by primary care prescriptions and long-term condition history. Beyond binary classification, Self-HR generalised to regression tasks — predicting incident and emergency admission durations — through fine-tuning alone, achieving the lowest MAE and RMSE across all tasks without repeat pretraining. These findings support Self-HR as a data-efficient and generalisable framework for readmission risk prediction in multimorbid populations, with potential to inform proactive discharge planning and targeted post-discharge care.