Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs
arXiv:2604.27899v1 Announce Type: new Abstract: Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central
arXiv:2608.26191v1 Announce Type: new Abstract: Incident risk prediction from longitudinal electronic health records (EHRs) is challenging because relevant signals are multimodal,
arXiv:2605.11398v1 Announce Type: new Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user
arXiv:2608.27847v1 Announce Type: new Abstract: Interactive medical diagnosis dynamically acquires patient information through multiple rounds of questioning, supporting accurate,
arXiv:2605.05482v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly being adopted across various domains. However, their adoption in banking industry faces
arXiv:2605.20188v1 Announce Type: cross Abstract: Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet
arXiv:2605.22833v1 Announce Type: cross Abstract: Chronic osteomyelitis presents substantial prognostic challenges due to its high recurrence risk and complex postoperative
arXiv:2604.04999v1 Announce Type: cross Abstract: Multimodal self-supervised pretraining offers a promising route to cancer prognosis by integrating histopathology whole-slide
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