GuardMLLM: Overconfidence-Aware Dynamic Fusion for Early Outcome Prediction.

Abstract

Early outcome prediction for intensive care unit (ICU) patients using multimodal electronic health records (EHRs) is crucial for timely intervention and resource allocation. Recent methods for clinical reasoning on EHRs using large language models (LLMs), which involve textualizing structured data and concatenating it with unstructured data for reasoning. However, these methods lose information about numerical trends because they convert structured data into static text. Furthermore, LLMs often exhibit overconfidence, producing highly certain but unreliable predictions. To address these issues, we propose GuardMLLM, a dynamic fusion framework that perceives overconfidence for multi-tasking early ICU outcome prediction.Specifically, GuardMLLM learns complex temporal relationships from structured data through temporal modeling and aggregation. Simultaneously, it transforms structured data into clinical narratives containing trend information and combines this with unstructured data. Task prediction is then performed using an LLM clinical inference engine. Furthermore, we designed a multi-indicator overconfidence detector and applied temperature calibration to quantify and correct unreliable LLM confidence. An adaptive, gated dynamic fusion module balances temporal representations and LLM-derived inference features to suppress LLM overconfidence.We verify our method on two large public medical datasets, Medical Information Mart for Intensive Care and Collaborative Research Database. Experimental results show that our method improves performance on tasks such as predicting patient mortality and ICU length of stay, and effectively alleviates overconfidence in LLM.