Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values [0.03%]
SHAP值可提高FDA授权的AI / ML脓毒症诊断工具的可解释性
Gregory L Watson,Grace Staples,Robin Carver et al.
Gregory L Watson et al.
Objectives: To assess the interpretability and acceptance of Shapley values for making artificial intelligence/machine learning (AI/ML) tools more transparent, interpretable, and useful to clinicians. ...
Automating eligibility assessment and enrollment for sugammadex administration within an integrated perioperative workflow [0.03%]
sugammadex（苏库马德克斯）给药资格评估和登记的自动化整合于一体化手术室工作流程中
Eilon Gabel,Stephen Murray,Tristan Grogan et al.
Eilon Gabel et al.
Background: Traditional clinical trial enrollment relies on manual screening and coordinator-led recruitment, creating scalability barriers in high-volume perioperative environments. This study evaluated whether a fully a...
Agent-based large language model system for extracting structured data from breast cancer synoptic reports: a dual-validation study [0.03%]
基于代理的大型语言模型系统从乳腺癌概要报告中提取结构化数据的双验证研究
Steven N Hart,Teya S Bergamaschi
Steven N Hart
Objectives: To develop and validate an agent-based Large Language Model (LLM) system for extracting structured data from breast cancer synoptic pathology reports and assess the performance gap between synthetic and real-w...
SynNER: syntax-infused named entity recognition in the biomedical domain [0.03%]
基于语法的医学文本命名实体识别方法研究
Muhammad Imran,Olga Zamaraeva,Carlos Gómez-Rodríguez
Muhammad Imran
Objective: This study evaluates the usefulness of explicit syntactic knowledge, integrated via a neural mechanism, in improving the accuracy of named entity recognition in the domain of biomedical text processing. ...
Correction to: Biomedical data repositories require governance for artificial intelligence/machine learning applications at every step [0.03%]
关于：生物医学数据仓库需要在每一步进行人工智能/机器学习应用治理
[This corrects the article DOI: 10.1093/jamiaopen/ooaf134.]. © The Author(s) 2026. Published by Oxford University Press on behalf of the American Medica...
Published Erratum
JAMIA open. 2026 Feb 23;9(1):ooaf173. DOI:10.1093/jamiaopen/ooaf173 2026
RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records [0.03%]
基于电子健康记录的强化学习增强标签高效主动表型构建（RELEAP）
Yang Yang,Kathryn I Pollak,Bibhas Chakraborty et al.
Yang Yang et al.
Objectives: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce annotation costs, but typical heuristics...
Large language models as medical code selectors: a benchmark using the International Classification of Primary Care [0.03%]
基于国际基层医疗分类的标准评估大型语言模型的医学代码选取能力
Vinicius Anjos de Almeida,Vinicius de Camargo,Raquel Gómez-Bravo et al.
Vinicius Anjos de Almeida et al.
Objectives: Medical coding structures health-care data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign International Classification of Primary Care...
A preliminary evaluation of AFibrisk: digital decision-support platform for atrial fibrillation risk assessment after cryptogenic stroke-a cross-sectional concordance study [0.03%]
AFiRisk的初步评估：隐源性卒中后房颤风险数字决策支持平台-A组横断面一致性研究
João Brainer Clares de Andrade,Rafael P Gomes,Alexandre Cristiuma Robles et al.
João Brainer Clares de Andrade et al.
Objectives: Identifying patients at high risk for atrial fibrillation (AF) after cryptogenic stroke remains a challenge, particularly in settings with limited access to long-term cardiac monitoring. The AFibrisk platform,...
Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites [0.03%]
改进死亡原因预测：多处医疗站点多模态数据机器学习模型的开发与验证
Mohammed Al-Garadi,Rishi J Desai,Kerry Ngan et al.
Mohammed Al-Garadi et al.
Objectives: To develop and validate machine learning (ML) models that predict probable cause of death (CoD) using structured electronic health record (EHR) data, unstructured clinical notes, and publicly available sources...
Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge [0.03%]
双重视角下护理人员关于人工智能预测的认知准备度研究：文字知识与要旨知识的解释力比较
Insook Cho,Soyun Shim,Hyunchul Park
Insook Cho
Objectives: The expansion of artificial intelligence (AI)-enabled clinical decision support (CDS) requires nurses to interpret complex model outputs. However, their cognitive readiness remains underexplored, particularly ...