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: 2105233
• 负责人: Yogatheesan Varatharajah
• 金额: $ 17.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105233&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.

尽管全国范围内神经科医生短缺，但目前的神经护理严重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。这一点在癫痫学领域尤为突出，在这个领域，癫痫学家花费大量的时间在视觉上检查和解释长时间的多通道脑电活动时间序列，称为脑电图（EEG）。这一负担不仅会导致癫痫病医生职业倦怠的加剧，还会在临床决策中引入审稿人偏见和潜在的错误。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梅奥诊所合作者的临床领域专业知识，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，医疗保健应用的自然吸引力将激发本科生和代表性不足的少数民族的兴趣。本研究将开发一套新颖的领域引导分析方法来处理时间序列脑电图数据，提取可操作的信息，为癫痫诊断提供临床决策支持。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颖可解释的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)利用基于深度学习的方法的廉价推理能力，开发全自动高效的脑电图预处理管道；b)设计新的机器学习模型，在领域专业知识的指导下，捕捉脑电图数据的时空动态；C)模型预测的解释和预测不确定性的量化，为临床决策提供支持；d)通过开发一种强大的分析工具，在现实世界中演示该框架，以加强对脑电图的专家审查并提高癫痫诊断的敏感性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts

• 发表时间: 2022-09
• 作者: Neeraj Wagh;Jionghao Wei;Samarth Rawal;Brent M. Berry;Y. Varatharajah

Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology

大规模临床脑电图的张量分解揭示了大脑生理学的可解释模式

• DOI: 10.1109/ner52421.2023.10123800
• 发表时间: 2023
• 期刊: 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER
• 作者: Gupta, Teja;Wagh, Neeraj;Rawal, Samarth;Berry, Brent;Worrell, Gregory;Varatharajah, Yogatheesan
• 通讯作者: Varatharajah, Yogatheesan

SCORE-IT: A Machine Learning Framework for Automatic Standardization of EEG Reports

SCORE-IT：脑电图报告自动标准化的机器学习框架

• DOI: 10.1109/spmb52430.2021.9672259
• 发表时间: 2021
• 期刊: IEEE Signal Processing in Medicine and Biology Symposium (SPMB
• 作者: Rawal, Samarth;Varatharajah, Yogatheesan
• 通讯作者: Varatharajah, Yogatheesan

Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy.

• DOI: 10.1111/epi.17257
• 发表时间: 2022-07
• 期刊: Epilepsia
• 影响因子: 5.6
• 作者: Varatharajah Y;Joseph B;Brinkmann B;Morita-Sherman M;Fitzgerald Z;Vegh D;Nair D;Burgess R;Cendes F;Jehi L;Worrell G
• 通讯作者: Worrell G

Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability Authors

脑电图数据的领域引导自我监督提高了下游分类性能和通用性

• 发表时间: 2021
• 期刊: Proceedings of Machine Learning Research
• 作者: Neeraj Wagh, Jionghao Wei