CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis

ACL ARR 2025 February Submission683 Authors

Abstract: Integrating multimodal clinical records—such as Electronic Health Records (EHR) and free-text clinical reports—has shown great potential in predicting clinical outcomes. However, prior work has primarily focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across different patients. These patterns, such as trends in vital signs like abnormal heart rate or blood pressure, can indicate deteriorating health or an impending critical event of any individual in a given population. Similarly, clinical notes often contain textual descriptions that reflect these changes. Identifying corresponding temporal patterns across different modalities is crucial for improving the accuracy of clinical outcome predictions, yet it remains a challenging task. To address this gap, we introduce a Cross-modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations and refines them using slot attention to generate temporal semantic embeddings. To ensure rich cross-modal temporal semantics in the learned patterns, we introduce a Temporal Pattern Noise Contrastive Estimation (TP-NCE) loss for cross-modal alignment, along with two reconstruction losses to retain core information of each modality. Evaluations on two clinically critical tasks—48 hour in-hospital mortality and 24-hour phenotype classification—using the MIMIC-III database demonstrate the superiority of our method over existing approaches. The code is anonymously available at https://anonymous.4open.science/r/MMMSPG-014C.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond, NLP Applications, Machine Learning for NLP

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 683