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.

尽管全国范围内缺乏神经科医生，但当今的神经科护理在很大程度上依赖于训练有素的工作人员对患者数据进行耗时的视觉审查。这在癫痫学领域中特别强调，其中癫痫学家花费大量时间在视觉上审查和解释大脑电活动的冗长的多通道时间序列，称为脑电图（EEG）。这种负担不仅导致癫痫病学家倦怠的升级，而且还在临床决策中引入了评审员偏见和潜在错误。该提案的目标是开发一个基于机器学习（ML）的决策支持框架，该框架与癫痫学家一起工作，并将他们的注意力集中在可操作的信息上。我们将利用伊利诺伊州的计算专业知识和我们在马约诊所的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重大创新，以实现上述目标。本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范例。我们也相信医疗应用的自然吸引力将激发本科生和代表性不足的少数群体的兴趣。本研究将开发一套新颖的领域引导的分析方法来处理时间序列EEG数据，提取可操作的信息，并为诊断癫痫提供临床决策支持。拟议研究的智力价值在于通过开发由临床领域专业知识指导的新型可解释机器学习架构来解决癫痫学领域未满足的需求。我们提出的工作包括，a）通过利用基于深度学习的方法的廉价推理能力，开发一个完全自动化和高效的EEG预处理管道; B）在领域专业知识的指导下，设计新颖的ML模型，捕获EEG数据的时空动态; c）解释模型预测和量化预测不确定性，以支持临床决策;以及d）通过开发一个强大的分析工具来增强EEG的专家评审并提高癫痫诊断的灵敏度，从而在真实的世界中展示该框架。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响评审标准进行评估，被认为值得支持。