This course explores how healthcare data is generated in practice - and how these processes influence the development of robust, trustworthy machine learning models. Students will examine how clinical workflows, documentation habits, and institutional practices introduce bias, noise, missingness, and spurious correlations into datasets. The course emphasizes the often-overlooked reality that many model failures stem not from algorithmic design, but from subtle flaws in how data is collected, labeled, and interpreted. Through lectures, real-world case studies, and observation of clinical data workflows, students will develop practical tools for understanding and improving data quality and model reliability. Topics include assessing data quality, addressing missingness and label leakage, developing scalable and accurate annotation strategies, leveraging synthetic data, and monitoring data and models for drift over time. Students will also learn to evaluate how these data challenges impact model generalization and robustness.
3 units · Medical Option (Med-Ltr-CR/NC)
This course explores how healthcare data is generated in practice - and how these processes influence the development of robust, trustworthy machine learning models. Students will examine how clinical workflows, documentation habits, and institutional practices introduce bias, noise, missingness, and spurious correlations into datasets. The course emphasizes the often-overlooked reality that many model failures stem not from algorithmic design, but from subtle flaws in how data is collected, labeled, and interpreted. Through lectures, real-world case studies, and observation of clinical data workflows, students will develop practical tools for understanding and improving data quality and model reliability. Topics include assessing data quality, addressing missingness and label leakage, developing scalable and accurate annotation strategies, leveraging synthetic data, and monitoring data and models for drift over time. Students will also learn to evaluate how these data challenges impact model generalization and robustness.
Offered in Winter 2027 at Stanford University.