Grant: Discovery Grants Program - Individual

BACKGROUND: The modern clinician is buried in data: Radiographic images, blood chemistry, clinical notes and more. Within, and especially between, these different sources of data lie critical insights into the health of patients. These insights, however, go mostly unobserved in contemporary clinical practice due to catastrophic information overload. MOTIVATION: Most machine learning methods employed in medical imaging were originally designed for computer vision with applications in very different domains than medical data. These methods cannot directly handle the challenge of heterogeneity (different image modalities, different acquisition protocols, different documentation formats, different time series) and non-standardized acquisition time in medical data. Moreover, they are usually designed to use very large training dataset (e.g. one million training samples with labelled ground truth), which is usually not possible in medical data applications. OBJECTIVES: Our aim is to form a multidisciplinary research program to fundamentally investigate the innovative machine learning handling heterogeneity for medical data analytics. Specifically, over 5 years, we will pursue our aim following these objectives, which explore: 1) the first unified feature extraction from both images and texts; 2) the first heterogeneous domains adaption; 3) the first next-generation computer-aided diagnosis system; 4) the longitudinal risk prediction systems. APPROACH: We will build on our previous success in fundamental regression learning and direct analytics system. Our newly proposed approaches will be based on the state-of-art machine learning (e.g. generative adversarial network) framework and our self-proposed optimization functions. Together they will provide a seamless analytic framework for all the data acquired ned from heterogeneous sources. And then these new approaches will be embedded in the multiple computer-aided diagnosis and prediction system to enable the new clinical applications. NOVELTY AND EXPECTED SIGNIFICANCE: This research will further enrich the fast-growing fields of machine learning and data science and move them from theoretical and fundamental science to practical engineering. It will enable prediction of diseases onset or progression and prognosis. This will allow physicians to better treat their patients and lead to improved health and quality of life for patients; to understand as much about a patient as possible, as early in their life as possible. This is crucial since identifying warning signs of serious illness at an early stage enables prevention and treatments that are far simpler and less expensive than those needed at later stages of the disease. This research will provide added value with no extra cost to existing clinical data and lead to more effective and efficient healthcare, which will benefit patients, physicians, and the broader healthcare system.