Non-invasive seizure forecasting system using e-diaries, internal and external factors

使用电子日记、内部和外部因素的无创癫痫发作预测系统

• 批准号: 10524393
• 负责人: Daniel M Goldenholz
• 金额: $ 18.98万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-05-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10524393
• 关键词: Affect; Age; Applied Research; Artificial Intelligence; Automobile Driving; Biometry; Biosensor; Brain; Calibration; Cessation of life; Clinical; Data; Data Science; Development; Disease; Dose; Drug resistance; Electroencephalography; Emergency department visit; Epilepsy; Exercise; Foundations; Fright; Goals; Heart Rate; Hormonal; Hormones; Hospitalization; Hour; Humidity; Impact Seizures; Informatics; Injury; Intractable Epilepsy; K-Series Research Career Programs; Knowledge; Laboratories; Location; Machine Learning; Mentors; Mentorship; Methods; Mission; Monitor; Morbidity - disease rate; National Institute of Neurological Disorders and Stroke; Outcome Study; Participant; Pathway interactions; Patients; Pattern; Persons; Pharmaceutical Preparations; Physicians; Predisposition; Public Health; Quality of life; Reporting; Research; Research Personnel; Research Project Grants; Risk; Running; Safety; Seizures; Sleep; Sleep Deprivation; Sleep Fragmentations; Stress; System; Techniques; Technology; Temperature; Temporal Lobe Epilepsy; The Sun; Time; Training; United States; Validation; Weather; Work; Wrist; aged; artificial intelligence algorithm; base; cohort; comorbidity; computer science; deep learning; design; diaries; digital; experience; follow-up; high risk; improved; longitudinal dataset; machine learning algorithm; medication compliance; meteorological data; mortality; multimodality; neglect; nervous system disorder; online course; prospective; risk prediction; side effect; skills; systematic review; tool; tool development; translational impact; wearable device; wearable sensor technology; weather patterns

This career development award will provide me with an opportunity to develop the needed skills to become an independent investigator using advanced machine learning and large-scale clinical cohorts applied to epilepsy. The research project centers on forecasting the risk of seizures non-invasively using electronic diaries (e-diaries) and biosensor data. It is unknown if non-invasive seizure forecasting can be sufficiently accurate to have clinical utility. My prior retrospective work suggests that using advanced machine learning algorithms to evaluate e-diary data (i.e., internal factors), forecasts of seizure risk are more accurate than chance forecasts. It is unknown if enhancing these forecasts using additional data from sleep biosensors, medication adherence, stress, weather patterns, stress, and exercise (i.e., external factors) would improve the accuracy further. Preliminary data suggest that this additional data may be valuable. For Aim 1, this project will prospectively validate the machine learning algorithm to forecast seizure risk in a cohort of people with epilepsy using e-diaries alone (internal factors). The forecasts will be compared with a rate-matched random forecast as a baseline. For Aim 2, the forecasts will be enriched using data from a wearable biosensor, automated medication adherence, as well as information about stress, hormonal cycles and weather (external factors). The expected outcome of this study is a validated method with higher accuracy for forecasting seizure risk using non-invasive techniques. In addition, this project includes educational objectives through mentorship and online courses and local coursework designed to prepare for research independence. The main educational objectives are (1) developing skills in advanced data science techniques, (2) managing a large clinical cohort, (3) build a strong biostatistics/informatics foundation, and (4) professional development. Dr. Brandon Westover, one of the foremost data science experts in the field of epilepsy, will serve as the primary mentor for this project. Additional mentorship will come from Dr. Jimeng Sun, an expert in machine learning and computer science, as well as Dr. Thomas Travison, an expert biostatistician and clinical trialist. My goal is to establish a state-of-the-art, independent laboratory focused on data science applied to decrease morbidity and mortality from epilepsy.

这个职业发展奖将为我提供一个发展所需技能的机会， 使用先进的机器学习和大规模临床队列应用于癫痫的独立研究者。 该研究项目的中心是使用电子日记（e-diaries）非侵入性地预测癫痫发作的风险 和生物传感器数据。目前尚不清楚无创性癫痫发作预测是否足够准确， 效用我之前的回顾性研究表明，使用先进的机器学习算法来评估电子日记， 数据（即，内部因素），癫痫发作风险的预测比偶然预测更准确。不清楚是否 使用来自睡眠生物传感器、药物依从性、压力、天气的额外数据来增强这些预测 模式、压力和锻炼（即，外部因素）将进一步提高准确性。初步数据 这表明这些额外的数据可能是有价值的。对于目标1，该项目将前瞻性地验证机器 学习算法预测癫痫患者队列中癫痫发作风险仅使用电子日记（内部 因素）。这些预测将与作为基线的速率匹配随机预测进行比较。对于目标2， 预测将使用来自可穿戴生物传感器、自动药物依从性以及 有关压力、荷尔蒙周期和天气（外部因素）的信息。本研究的预期结果是 使用非侵入性技术预测癫痫发作风险的准确性更高的有效方法。此外，本发明还提供了一种方法， 该项目包括通过导师制、在线课程和本地课程实现教育目标 旨在为研究独立做准备。主要的教育目标是：（1）培养 先进的数据科学技术，（2）管理大型临床队列，（3）建立强大的 生物统计学/信息学基础;（4）专业发展。布兰登·韦斯特弗医生， 癫痫领域最重要的数据科学专家将担任该项目的主要导师。额外 导师将来自机器学习和计算机科学专家孙继孟博士，以及 托马斯崔维森，生物统计学家和临床试验专家。我的目标是建立一个最先进的 独立实验室专注于应用数据科学来降低癫痫的发病率和死亡率。