CRII: SCH: Domain-guided Machine Learning for Clinical Decision Support in Epilepsy

CRII：SCH：用于癫痫临床决策支持的领域引导机器学习

• 批准号: 2344731
• 负责人: Yogatheesan Varatharajah
• 金额: $ 17.5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344731&HistoricalAwards=false
• 关键词: CRII; SCH; Domain; guided; Machine

Despite the nationwide shortage of neurologists, present-day neurological care relies heavily on time-consuming visual review of patient data by trained staff. This is particularly emphasized in the field of epileptology where epileptologists spend a substantial amount of their time on visually reviewing and interpreting lengthy multi-channel time series of brain electrical activity, called electroencephalography (EEG). This burden not only contributes to the escalation of epileptologist burnout, but also introduces reviewer bias and potential errors in clinical decisions. The goal of this proposal is to develop a machine-learning (ML)-based decision support framework that works together with epileptologists and focuses their attention to actionable information. We will leverage the computing expertise of Illinois and the clinical domain expertise of our collaborators at the Mayo Clinic and demonstrate significant innovations across the data-science lifecycle to achieve the aforementioned goal. The data and the methods utilized in this research will serve as examples in advanced interdisciplinary classes and training healthcare professionals. We also believe that the natural appeal of healthcare applications will stimulate the interest of undergraduates and underrepresented minorities.This research will develop a set of novel domain-guided analytical methods to process time-series EEG data, extract actionable information and provide clinical decision support for diagnosing epilepsy. The intellectual merit of the proposed research is in addressing an unmet need in the field of epileptology through the development of novel explainable machine learning architectures guided by clinical domain expertise. Our proposed work includes, a) development of a fully automated and efficient EEG preprocessing pipeline by leveraging the cheap inference capability of deep learning-based approaches; b) designing novel ML models, guided by domain expertise, that capture the spatio-temporal dynamics of EEG data; c) interpretation of model predictions and quantification of prediction uncertainty for clinical decision support; and d) demonstration of the framework in the real world by developing a robust analytical tool to augment expert review of EEGs and improve the sensitivity of epilepsy diagnosis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管在全国范围内短缺神经科医生，但当今的神经护理在很大程度上依赖于训练有素的员工对患者数据的耗时审查。这在癫痫学领域尤其强调，在癫痫学领域中，癫痫学家花费大量时间在视觉上审查和解释冗长的多渠道时间序列的脑电活动（称为脑电图（EEG））上。这种负担不仅有助于癫痫学家倦怠的升级，而且还引入了审查者的偏见和临床决策的潜在错误。该建议的目的是开发与癫痫学家一起工作的基于机器学习（ML）的决策支持框架，并将注意力集中在可行的信息上。我们将利用伊利诺伊州的计算专业知识以及Mayo Clinic合作者的临床领域专业知识，并在数据科学生命周期中展示了重大创新，以实现上述目标。这项研究中使用的数据和方法将作为高级跨学科课程和培训医疗保健专业人员的例子。我们还认为，医疗保健应用的自然吸引力将刺激本科生和代表性不足的少数民族的兴趣。这项研究将开发一系列新型的领域引导的分析方法来处理时间序列EEG EEG数据，提取可行的信息，并为诊断性疾病提供临床决策支持。拟议研究的智力优点在于通过开发以临床领域专业知识为指导的新型可解释的机器学习体系结构来解决癫痫学领域的需求。我们提出的工作包括a）通过利用廉价的基于深度学习的方法的廉价推理能力来开发完全自动化和有效的脑电图预处理管道； b）设计在域专业知识的指导下设计的新型ML模型，以捕获脑电图数据的时空动力学； c）解释模型预测和量化临床决策支持预测不确定性的量化； d）通过开发一种强大的分析工具来增强脑电图的专家审查并提高癫痫诊断的敏感性，以展示现实世界中的框架。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。