TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Prediction
We propose TRACE (TRAjectory modeling of multimodal Clinical Events), a daily-scale framework organizing clinical events into day-level trajectories, separating day-invariant global context from day-specific local events drawn from EHR data, chest x-rays, radiology reports, and discharge summaries.
ProofPaper ↗
Key points
- Accurate inpatient risk prediction requires modeling heterogeneous clinical evidence that accumulates irregularly over time.
- Existing multimodal approaches combine structured health record data, chest x-rays, and clinical text, but primarily focus on modality fusion or missing-modality compensation, with limited attention to temporal trajectories over an admission.
- A phenotype-guided contrastive objective further organizes admissions under heterogeneous disease distributions.
- Ablation studies validate our attention masks and contrastive learning, while trajectory-level attribution localizes risk-associated hospital days and modalities.
Sources (1)
- [1]TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk PredictionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:25 AM
We propose TRACE (TRAjectory modeling of multimodal Clinical Events), a daily-scale framework organizing clinical events into day-level trajectories, separating day-invariant global context from day-specific local events drawn from EHR data, chest x-rays, radiology reports, and discharge summaries.
Accurate inpatient risk prediction requires modeling heterogeneous clinical evidence that accumulates irregularly over time.
Extractive summary: sentences quoted from the sources.
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