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）： 在以前的工作中，我们已经开发，部署和评估了一种新的智能医疗培训系统的病理诊断和报告。SlideTutor是一个个性化的，自适应的模拟环境，提供针对每个学生需求的解释和帮助。我们的研究结果表明，该系统在诊断和报告的准确性产生显着的改善。平均而言，学生的成绩提高了400%，并随着时间的推移保留了这些技能。我们的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，以便它可以适应其干预措施。在这个过程中，SlideTutor系统捕获了大量关于医生推理中间步骤的信息。我们已经开发了一个精心设计的研究基础设施，使我们能够利用这些丰富的信息作为研究数据。有了这个新的研究基础设施，我们准备就如何为培训医生创建自适应和个性化的系统做出更一般的陈述。我们现在建议利用SlideTutor基础设施来关注三个基本领域，这些领域在医学领域几乎没有以前的研究：元认知，性能预测和学习行为。除了指导未来医疗培训系统的发展外，这些领域的工作有可能对患者安全、医疗模拟和基于能力的评估等领域产生深刻影响。