数据资源deidentified structured EHR tablescritical care EHR datasetLarge-scale hospital and ICU EHR dataset; version 3.1申请访问 MIMIC-IV v3.1 重症监护与住院 EHR 数据集
MIMIC-IV is a large deidentified electronic health record dataset from Beth Israel Deaconess Medical Center, covering hospital and ICU data for critical care research. It is a core benchmark source for clinical prediction, temporal EHR modeling, phenotyping, and healthcare AI method development.
技术竞赛Submissions due 2026-07-01 11:00 BeijingHealthcare AI applicationFHIR and clinical data截止 北京时间 2026-07-01 11:00 HL7 2026 AI 挑战
HL7 healthcare AI challenge focused on AI applications around health data interoperability and standards-based workflows.
征稿与合作Design for Augmented Humanity截止 北京时间 2026-05-30期刊专刊 Design for Augmented Humanity 专刊:以人为本与增强型医疗 AI
This SAGE / Design for Augmented Humanity special issue calls for papers on human-centred and augmented healthcare AI, including theories, applications, and methodologies. The CFP lists a paper submission deadline of 2026-05-30 and is relevant to clinical translation, trustworthy and explainable medical AI, human-AI collaboration, and clinical workflow AI.
征稿与合作INFORMS Journal on Data Science截止 北京时间 2026-09-15期刊专刊 INFORMS Journal on Data Science 专刊:医疗人工智能与数据科学
The INFORMS Journal on Data Science CFP focuses on artificial intelligence and data science for healthcare. It is relevant to medical AI work on clinical prediction, decision support, operations, fairness, reliability, and deployment of data-driven healthcare systems. The CFP page lists a 2026 special issue call and submission timeline.
征稿与合作PRICAI 2026截止 北京时间 2026-06-13会议征稿 PRICAI 2026 征稿
CCF-Deadlines lists PRICAI 2026 with papers due 2026-06-13 UTC-12 and conference dates 2026-11-17 to 2026-11-20 in Guangzhou. PRICAI is a general AI venue relevant to medical AI methods, clinical decision support, medical agents, and trustworthy healthcare AI.
MIT OpenCourseWare:临床数据学习、可视化与部署
MIT OCW HST.953 focuses on practical considerations for operationalizing machine learning in healthcare settings. It is relevant for learners moving from clinical data modeling into visualization, deployment, workflow, and real-world healthcare AI implementation.