FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature of healthcare data and the need for privacy-preserving solutions. This paper proposes FEDI-CODE, a Federated and Causally Informed Dementia Estimation framework that integrates deep learning, federated learning, and counterfactual inference to predict dementia risk across distributed patient data sources. FEDI-CODE is designed to operate without centralizing sensitive medical data, enabling collaborative training across institutions while preserving privacy. It combines temporal modeling of longitudinal imaging and clinical data with individualized treatment effect estimation for modifiable risk factors such as alcohol consumption, weight, and cardiovascular indicators. A fusion module aggregates representations from each site to form a global prediction head. Extensive experiments on simulated multi-site dementia datasets demonstrate that FEDI-CODE achieves an accuracy of 83.7%, a precision of 83%, a recall of 81%, an F1-score of 82%, and an AUC-ROC of 0.86, outperforming standard federated models and deep learning baselines by notable margins. The model also generalizes well to external datasets, achieving 79.2% accuracy and 0.80 AUC-ROC, confirming its robustness. Furthermore, FEDI-CODE produces interpretable causal insights by estimating individual treatment effects, offering actionable clinical value. These results highlight FEDI-CODE as a scalable, interpretable, and privacy-aware solution for early dementia screening and personalized risk assessment.