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

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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.