BMIF 204

BMIF 204: Foundations of Clinical Data and Its Applications provides a hands-on experience of how clinical data drives modern healthcare and bioinformatics. Spanning 13 weeks, the course begins with a theoretical foundation (Weeks 1–7), exploring clinical decision-making, EHR systems, and the flow of data from clinical care to research. Students will examine multiple data sources—ranging from electronic health records to claims datasets- and ICU records, -while considering interoperability standards (HL7/FHIR) and the complexities of data normalization, validation, and storage.

Core topics include disease prediction, treatment outcome prediction, causal inference principles, target trial emulation, and real-world evidence generation. The course addresses critical challenges such as high-dimensionality, time-related biases, and temporal shifts in data, emphasizing ethical and regulatory considerations like privacy, fairness, and historical biases in healthcare. Students will learn predictive modeling and risk stratification techniques, leveraging machine learning tools, feature selection methods, and longitudinal data analyses.

During the second half of the course (Weeks 8–13), students transition to a hands-on lab format, working collaboratively on two projects with actual EHR and claims data. Through the structured final projects, they will gain practical experience in data preprocessing, causal study designs, and advanced analytics. By the end of the course, participants will have the skills to clean, integrate, and analyze complex clinical datasets, communicate ethical implications, and design robust, data-driven solutions for pressing healthcare challenges.

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