arXiv AI July 22, 2026 By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.

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

arXiv Machine Learning

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MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

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llms benchmarks

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EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

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nlp multimodal

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HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care

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multimodal benchmarks safety

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ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).

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llms multimodal benchmarks safety

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A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models

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Related stories

MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care

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ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

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