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Knowledge-Augmented Large Language Model for Multimodal Electronic Health Record-Based Risk Prediction: Development

Rituparna Datta¹, Jiaming Cui², Zihan Guan¹

¹University of Virginia, Charlottesville, VA, United States.

|June 12, 2026

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This summary is machine-generated.

KAMELEON, a novel framework, improves clinical outcome prediction by integrating electronic health records and biomedical knowledge. This approach significantly enhances risk prediction accuracy, outperforming existing methods.

Area of Science:

Computational biology and bioinformatics
Machine learning in healthcare
Clinical informatics

Background:

Accurate clinical outcome prediction from electronic health records (EHRs) is vital for patient care and resource management.
Existing machine learning models face challenges with EHR multimodality, long clinical note contexts, and imbalanced data.
Integrating structured EHR data with unstructured clinical notes and external knowledge is needed for improved prediction.

Purpose of the Study:

To introduce and evaluate KAMELEON (Knowledge-Augmented Multimodal EHR Learning for Outcome Prediction), a hybrid framework.
To enhance clinical risk prediction by integrating diverse EHR modalities and external biomedical knowledge.
To assess KAMELEON's performance against established models and large language models (LLMs).

Keywords:

EHR biomedical knowledge graphs clinical risk prediction electronic health records knowledge-augmented reasoning large language models machine learning multimodal data integration

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Main Methods:

Utilized the Medical Information Mart for Intensive Care-III (MIMIC-III) dataset (over 40,000 ICU patients).
Evaluated 30-day readmission and in-hospital mortality prediction using patient-disjoint 80:20 train-test splits.
Compared KAMELEON against general/medical LLMs and structured baselines using AUROC, AUPRC, and macro F1-score metrics.

Main Results:

KAMELEON consistently outperformed all evaluated baselines across prediction tasks.
The KAMELEON-balanced random forests model achieved an AUROC of 0.85 for 30-day readmission prediction.
The KAMELEON-extreme gradient boosting model achieved an AUROC of 0.92 and AUPRC of 0.650 for in-hospital mortality prediction.
LLM-generated reasoning was critical, with its removal significantly degrading performance (AUROC dropped from 0.85 to 0.7).

Conclusions:

KAMELEON is a pioneering framework enhancing LLMs for healthcare prediction via graph-guided knowledge retrieval and structured ML.
The framework demonstrates superior performance, validating the synergistic value of multimodal data and LLM reasoning.
KAMELEON offers a robust approach to clinical risk estimation, addressing limitations of prior predictive models.