Through the Years
BMIR has a long history at Stanford. We began in 1979 as the Medical Computer Science (MCS) group of the Stanford Knowledge Systems Laboratory (KSL). Work in the 1970s on computer systems such as MYCIN and other expert systems provided strong evidence that biomedical knowledge could be modeled computationally and processed to inform clinical decision making. MCS was the unit in the School of Medicine where such work would be continued.
During the 1980s, Ted Shortliffe’s work on ONCOCIN, an intelligent system to provide decision support to clinicians caring for patients who had breast cancer and lymphoma, provided the centerpiece for a number of well known projects. The problem of building knowledge bases for ONCOCIN to encode the procedures for applying cancer chemotherapy protocols led to Mark Musen’s early work on the Protégé system. The challenge of reasoning about the temporal relationships among patient data values and therapeutic interventions led to Michael Kahn’s work on a system known as TOPAZ.
During this time, Greg Cooper had identified ways to overcome limitations of naïve Bayesian reasoning, and led a group of students who investigated various aspects of Bayesian belief networks. This pioneering research offered some of the first practical demonstrations of belief-network technology.
A this time, the Medical Computer Science group was supported by the Stanford University Medical Experimental Computer for Artificial Intelligence in Medicine (SUMEX-AIM), the brainchild of Joshua Lederberg and Ed Feigenbaum in the 1970s that became the major national resource that sustained research on intelligent systems in biomedicine for more than 15 years.
Subsequently, the unit received infrastructure support from a resource grant that created the Center for Advanced Medical Informatics at Stanford (CAMIS).
In 1988, the Medical Computer Science Group reorganized to become a section of the Division of General Internal Medicine of the Department of Medicine. In 1992, it became its own freestanding division. Initially known as Stanford Medical Informatics, BMIR adopted its current name in 2007 to emphasize its broad scope and its emphasis on biomedical informatics research.
The 1990s saw the development and deployment of a decision-support system for management of patients living with HIV infection as the next major focus of research. Known as T-Helper, the project spawned Amar Das’ development of the Chronus temporal data management system and Yuval Shahar’s work on a system known as Résumé, which used application-specific knowledge to abstract data into meaningful abstract intervals. T-Helper provided a domain-independent infrastructure for offering decision-support for protocol-based therapy known as EON. EON became the foundation of the ATHENA suite of decision-support tools for guideline-based care of patients with chronic diseases. Developed in collaboration with Mary Goldstein, EON has been installed at several VA medical centers.
In 2005, BMIR became home to the National Center for Biomedical Ontology (NCBO). The NCBO is a ten-year project supported by the NIH program of National Centers for Biomedical Computing. The NCBO hosts a repository of most of the world’s publicly accessible biomedical ontologies and terminologies, and builds tools and Web services that enable biomedical scientists and clinicians to put those ontologies to use. Among the most widely used services is the NCBO Annotator, which Nigam Shah conceived as a mechanism for the automated association of textual data with appropriate controlled terms. The NCBO Annotator has become the foundation for ongoing work to perform text mining of patient-record data to identify evidence of adverse drug events and of other indicators reflecting the quality of medical care.
The Quantitative Sciences Unit joined BMIR in 2013 to facilitate its mission of collaborative consultation in both biostatistics and biomedical informatics.