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

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.