Fall 2026 Missouri Data Science and Informatics Symposium

Healthcare and biomedical research generate data at unprecedented volume and complexity, yet translating these data into actionable decisions remains a fundamental challenge at every level, from the bedside to research policy. In this talk, I present a series of our studies illustrating how data science methods, including machine learning, natural language processing, computable phenotyping, and network analysis, can serve as a decision engine across diverse domains. This shows how computable phenotype analyses within a national clinical research network reveal meaningful variation in medication adherence by patient groups, disease stage, and rurality; how explainable machine learning models can improve prognostic risk stratification in heart disease and cardiac imaging; and how foundation transformer models and large language models are reshaping diagnostics and patient communication. Beyond the clinic, I discuss solutions for data interoperability and harmonization in genomic and precision medicine, and present text-mining and network analyses that uncover hidden patterns in NIH funding, relevant to anyone invested in the future of research funding. Together, these projects illustrate a unifying principle: that rigorous, transparent data science can strengthen decision-making not only in how we diagnose and treat disease, but also in how we communicate findings to patients, harmonize data across systems, and allocate research resources.