arXiv AI August 14, 2026 By Harshavardhan Battula, Jiacheng Liu, Jaideep Srivastava

Enhancing In-Hospital Mortality Prediction Using Multi-Representational Learning with LLM-Generated Expert Summaries

arXiv:2411. 16818v2 Announce Type: replace-cross Abstract: To evaluate a multi-representational framework in which large language model (LLM)-generated expert summaries of intensive care unit (ICU) notes are fused with physiology for in-hospital mortality (IHM) prediction, and to determine how much of the resulting gain is non-redundant with the notes themselves.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Large Language Models as Unified Multimodal Learners for Clinical Prediction

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MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncology

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LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.

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Primary ICD Category Prediction using LLM-based Probing

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Removing Temporal Note Redundancy Improves Multimodal Reinforcement Learning for Medicine

arXiv:2608. 14157v1 Announce Type: new Abstract: Mechanical ventilation is a critical life-support intervention, requiring dynamic adjustments to ventilator settings as a patient's condition evolves.

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CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction

arXiv:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.

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Large Language Models as Unified Multimodal Learners for Clinical Prediction

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