IBIIS & AIMI Seminar: Fusion of Multi-Modal Data Stream for Clinical Event Prediction – Simulating Physician’s Workflow

April 21, 2021 @ 12:00 pm – 1:00 pm
Zoom: https://stanford.zoom.us/j/99400230924?pwd=YnlZenJlOVMwRW42TmRiSVM1ZlNWZz09
Ramzi Totah

Imon Banerjee, PhD
Assistant Professor
Co-director Medical and Health Informatics Core (MHIC)
Department of Biomedical Informatics
Department of Radiology
Member of Winship Cancer Institute
Emory School of Medicine

Fusion of Multi-Modal Data Stream for Clinical Event Prediction – Simulating Physicians’ Workflow

Advancements in machine learning and deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields of diagnosis, prognosis, and treatment decisions. However, most of current state-of-the-art machine learning and deep learning models for healthcare applications consider only a single input data stream without data informing clinical context. The trend of ignoring clinical contextual information is particularly prominent when dealing with the diagnosis and prognosis tasks where the imaging data is accessible. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using machine learning and increase the physician trust, clinical diagnosis and prognosis models must also achieve the capability to process contextual clinical data from electronic health records (EHR) in addition to pixel or other sensor data. This talk will present multiple fusion machine learning models on the imaging data with boosted performance by integrating the clinical context. In addition to imaging, I will also present a smart flexible sensor patch with on-chip AI capability that can be used in homecare to generate advance alert of cardiovascular abnormality by combining physiological signal data with patient demographic and comorbidity information.