Federated ICU mortality prediction
- AI/ML
- Data
- Context
- Research internship · Washington State University
- Period
- Sep 2025 – Mar 2026
- Status
- Paper under review · IEEE Trans. Dependable & Secure Computing
A multimodal federated learning pipeline for predicting ICU mortality on the eICU dataset, covering 10,000 patients from 140 hospitals.
- Designed FixedFusionNet, a three-branch model (BiLSTM + TF-IDF + note-type) that keeps any one modality from dominating — the “modality collapse” problem in distributed training.
- Proposed a local weighted BCE correction that cut per-hospital variance by 32.4% compared with FedAvg.
- Built and version-controlled the pipeline in Git, refining it through twice-weekly reviews with two mentors.
- AUROC · FixedFusionNet + FedProx
- 0.7406
- deaths detected
- 120/217
- per-hospital variance vs. FedAvg
- −32.4%
Built with: Federated learning / FedProx / FedAvg / BiLSTM / TF-IDF / Git