输入关键词或点击标签，按论文、数据资源、竞赛截止日期、征稿与课程缩小范围。 标签：EHR 与临床预测 范围：课程资源
Stanford MED277 / CS337 AI-Assisted Health Care examines how to move technical advances into clinical settings, choose the right healthcare problems, and improve outcomes that matter. It is geared toward learners who want to use AI and machine learning for real-world human health impact.
MIT OCW 6.S897 Machine Learning for Healthcare introduces clinical data and machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, medical imaging, public health, and clinical workflow improvement.
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.
This University of Colorado System course introduces how AI shapes modern health systems, including key applications, adoption trends, digital transformation, health equity, and opportunities for addressing grand healthcare challenges. Coursera lists it as beginner level and 8 hours.
MIT course materials for machine learning methods in healthcare and clinical data.