Biomedical systems translate complex biological and medical data into computable formats that can be used for tasks such as diagnosis, decision support, or drug discovery. At the core of these systems is the challenge of creating fully computable, machine-actionable models of biomedical information so that knowledge can be shared and reused across different applications. This course explores methods for modeling biomedical systems, including topics in knowledge representation, controlled terminologies, ontologies, interoperability, and symbolic AI. Students will acquire hands-on experiences with several systems and tools, such as Protégé, Fast Healthcare Interoperability Resources (FHIR), and semantic web technologies. Prerequisites: CS 106A or equivalent. Basic familiarity with programming, probability, and logic are helpful for taking this course.
3 units · Medical Option (Med-Ltr-CR/NC)
Biomedical systems translate complex biological and medical data into computable formats that can be used for tasks such as diagnosis, decision support, or drug discovery. At the core of these systems is the challenge of creating fully computable, machine-actionable models of biomedical information so that knowledge can be shared and reused across different applications. This course explores methods for modeling biomedical systems, including topics in knowledge representation, controlled terminologies, ontologies, interoperability, and symbolic AI. Students will acquire hands-on experiences with several systems and tools, such as Protégé, Fast Healthcare Interoperability Resources (FHIR), and semantic web technologies. Prerequisites: CS106A or equivalent. Basic familiarity with programming, probability, and logic are helpful for taking this course.
Offered in Spring 2027 at Stanford University.