arXiv:2607. 09880v1 Announce Type: cross Abstract: Clinical time series are central to patient monitoring, risk assessment, and clinical decision support.
arXiv:2607. 25947v1 Announce Type: new Abstract: Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications.
MMTClinic is a new benchmark that tests large language models on complex reasoning and question‑answering tasks involving clinical time‑series data. It combines text, medical images, and multivariate physiological signals to create 30,000 QA pairs—including 15,000 multiple‑choice and 15,000 open‑ended questions—in five languages (English, Hindi, Bengali, Marathi, and Tamil). The benchmark covers mortality prediction, heart‑rate forecasting, and SOFA score estimation, and evaluates 13 state‑of‑the‑art LLMs across zero‑shot, few‑shot, and chain‑of‑thought settings, revealing significant performance gaps across tasks, languages, and modalities.
arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.
arXiv:2607. 09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making.
The paper introduces BRIE, a continuously maintainable benchmark for evaluating large language models (LLMs) in electronic health record (EHR) information retrieval. It presents a scalable framework that automatically generates question–answer pairs from longitudinal EHR notes, validated by nineteen clinicians. The benchmark allows assessment of multiple inference strategies and highlights that state‑of‑the‑art LLMs often miss clinically important information, especially when synthesis across documents is required.
The paper introduces BRIE, a scalable framework that automatically creates question–answer pairs from longitudinal electronic health record notes, validated by nineteen clinicians. It offers a continuously maintainable benchmark for evaluating large language models in clinical settings, addressing limitations of manual, costly, and quickly outdated existing benchmarks. Experiments across nine LLMs and five inference strategies reveal that even state‑of‑the‑art systems often miss clinically important information, especially for synthesis‑heavy queries.
arXiv:2607. 26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured-table reasoning, or generic scientific repositories.
In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for reliable assessment of disease progression and treatment response.
arXiv:2603. 06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning.
arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.
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.
arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.