Computational Methods for Personalized and Adaptive Cognitive Training

个性化和适应性认知训练的计算方法

• 批准号: 7523638
• 负责人: Rebecca S Jacobson
• 金额: $ 95.63万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7523638
• 关键词: Area; Awareness; Behavior; Classification; Cognitive; Computing Methodologies; Data; Dependency; Detection; Development; Diagnosis; Diagnostic; Environment; Frequencies; Future; Individual Differences; Intervention; Knowledge; Learning; Measurement; Medical; Methods; Metric; Modeling; Motivation; Pathologic; Performance; Physicians; Process; Reporting; Research; Research Infrastructure; Students; System; Testing; Time; Training; Visual; Work; base; cognitive training; computer based statistical methods; heuristics; improved; markov model; network models; novel; patient safety; simulation; skills; therapy design

DESCRIPTION (provided by applicant): In previous work, we have developed, deployed and evaluated a novel intelligent medical training system for pathologic diagnosis and reporting. SlideTutor is an individualized, adaptive, simulation environment that provides explanations and assistance specific to each student's needs. Our results show that the system produces dramatic improvements in diagnostic and reporting accuracy. On average, students achieve a 400% gain in performance, and retain these skills over time. A unique aspect of our system is that it dynamically models student skills, knowledge and misconceptions, so that it can adapt its interventions. In the process, the SlideTutor system captures an enormous amount of information about the intermediate steps in the physician reasoning. We have developed an elaborate research infrastructure that allows us to make use of this abundant information as research data. With this novel research infrastructure, we are poised to make far more general statements about how to create adaptive and individualized systems for training physicians. We now propose to leverage the SlideTutor infrastructure to focus on three foundational areas where there is nearly no previous research in medical domains: metacognition, performance prediction, and learning behaviors. Work in these areas has the potential to deeply impact the fields of patient safety, medical simulation and competency- based assessment, in addition to guiding the development of future medical training systems.

描述（由申请人提供）： 在之前的工作中，我们开发、部署和评估了一种用于病理诊断和报告的新型智能医疗培训系统。 SlideTutor 是一个个性化、自适应的模拟环境，可针对每个学生的需求提供解释和帮助。我们的结果表明，该系统显着提高了诊断和报告的准确性。平均而言，学生的成绩提高了 400%，并随着时间的推移保留这些技能。我们系统的一个独特之处在于，它动态地模拟学生的技能、知识和误解，以便能够调整干预措施。在此过程中，SlideTutor 系统会捕获有关医生推理中间步骤的大量信息。我们开发了完善的研究基础设施，使我们能够利用这些丰富的信息作为研究数据。有了这个新颖的研究基础设施，我们准备就如何创建用于培训医生的适应性和个性化系统做出更普遍的陈述。我们现在建议利用 SlideTutor 基础设施重点关注医学领域之前几乎没有研究的三个基础领域：元认知、表现预测和学习行为。除了指导未来医疗培训系统的开发外，这些领域的工作还有可能对患者安全、医疗模拟和基于能力的评估领域产生深远影响。