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: 7849693
• 负责人: Rebecca S Jacobson
• 金额: $ 94.56万
• 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 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7849693
• 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）： 在先前的工作中，我们已经开发，部署和评估了一种新型的智能医学培训系统，用于病理诊断和报告。 Slidetutor是一个个性化的，自适应的模拟环境，可为每个学生的需求提供特定的解释和帮助。我们的结果表明，该系统在诊断和报告准确性方面产生了显着改善。平均而言，学生的表现增长了400％，并随着时间的推移保留这些技能。我们系统的一个独特方面是，它动态地对学生的技能，知识和误解进行了动态建模，以便可以调整干预措施。在此过程中，Slidetutor系统捕获了有关医师推理中中间步骤的大量信息。我们已经开发了一个精心设计的研究基础设施，使我们能够将这些丰富的信息用作研究数据。有了这个新颖的研究基础设施，我们准备就如何为培训医师创建适应性和个性化的系统做出更一般的陈述。现在，我们建议利用Slidetutor基础设施专注于几乎没有医疗领域研究的三个基础领域：元认知，绩效预测和学习行为。除指导未来的医疗培训系统的发展外，在这些领域的工作有可能深深影响患者安全，医疗模拟和基于能力的评估领域。