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Multimodal Prompt Learning Improves Critical Care Patient Monitoring with Incomplete EHRs

Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu· August 25, 2026 View original

Key takeaways

  • A new multimodal prompt-learning framework improves critical care patient prediction with incomplete EHRs.
  • Four types of prompts (generative, missing-signal, missing-type, temporal) address different aspects of missing data.
  • The framework captures both intramodal dependencies and cross-modal interactions effectively.
  • It significantly outperforms existing methods in various missing-modality scenarios, enhancing robustness.

Who benefits

HealthcarePharmaceuticalsMedical Devices

Summary

A new multimodal prompt-learning framework is proposed to enhance critical care patient assessment by robustly handling incomplete electronic health records (EHRs). This framework uses four types of prompts to manage missing data, improving prediction accuracy even when physiological time series or clinical notes are partially or entirely unavailable.

Accurate patient assessment in intensive care units (ICUs) is vital for timely interventions and better outcomes. Multimodal electronic health records (EHRs), which combine structured physiological data and clinical notes, offer comprehensive information for predicting patient conditions. However, in real-world scenarios, these data streams are often incomplete, leading to performance degradation in existing multimodal AI models. To overcome the challenge of missing data, researchers have developed a multimodal prompt-learning framework designed for robust clinical prediction. This framework introduces four distinct types of prompts: generative prompts, missing-signal prompts, missing-type prompts, and temporal prompts. Generative prompts create substitute representations for unavailable data, while missing-signal prompts distinguish between observed and generated information. Missing-type prompts adapt the model to various data availability configurations, and temporal prompts aggregate information from time-encoded clinical sequences. This integrated approach allows the model to capture both dependencies within individual data types and interactions across different data types, even with incomplete information. Extensive experiments confirm that this method significantly outperforms existing approaches in various missingness scenarios.

Why it matters

Healthcare professionals and AI developers in medical settings can leverage this framework to build more reliable predictive models for critical care. It addresses a major practical hurdle—incomplete data—making AI tools more robust and applicable in real-world clinical environments.

How to implement this in your domain

  1. 1Evaluate existing clinical AI models for their robustness to missing data in multimodal EHRs.
  2. 2Explore prompt-learning techniques to enhance the performance of AI systems when dealing with incomplete patient data.
  3. 3Collaborate with data scientists to implement and test multimodal prompt-learning frameworks in critical care prediction tasks.
  4. 4Develop strategies for integrating AI-driven predictive insights into clinical workflows, considering data availability constraints.

Original post by Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu

"arXiv:2608.21941v1 Announce Type: new Abstract: Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal electronic health records (EHRs), including structured physiological time series…"

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Originally posted by Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu on X · view source

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