The paper introduces EHRBench, an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making, addressing the insufficient understanding of LLMs' reliability in real-world clinical tasks. Its goal is to ensure both scale and quality in the evaluation of Clinical Decision Making (CDM) models.
This paper introduces ClinicalBench, a 400-question benchmark designed to stress-test assertion-aware retrieval for cross-admission clinical QA on MIMIC-IV using real EHR notes. It also presents EpiKG, a patient knowledge graph system that incorporates assertion and temporality tags to route retrieval by question intent, demonstrating significant performance improvements across various LLMs.
HypEHR is a compact Lorentzian model utilizing hyperbolic geometry to address Electronic Health Record (EHR) question answering, overcoming cost and hierarchical structure challenges of LLM-based methods. It is pretrained for next-visit diagnosis prediction and alignment with medical ontologies, achieving LLM-comparable performance with significantly fewer parameters.
This research introduces "Schema-Adaptive Tabular Representation Learning," a novel method using Large Language Models (LLMs) to generate transferable tabular embeddings. By semantically encoding structured variables into natural language, it enables zero-shot alignment across varying EHR schemas in clinical medicine without manual feature engineering.
GraphDiffMed is a new framework for recommending safe and effective medication combinations from electronic health records (EHRs). It applies dual-scale Differential Attention to filter noisy signals and incorporates pharmacological constraints during learning, significantly improving recommendation quality.
This research validates a deep learning algorithm for glaucoma risk assessment using systemic electronic health records. The model, fine-tuned on Stanford patient data, achieved an AUROC of 0.883 and PPV of 0.657, showing strong potential for scalable and accessible pre-screening.