News & Announcements
Discover the latest research developments and scientific achievements of the BMIR team
The gap in our ability to systematically evaluate how clinical decision support affects how clinicians think and behave hinders the design and implementation of safe and effective AI for patient care. BMIR researchers aim to create a proof of concept of a method for generating a comprehensive, low-bias feature pool of downstream clinical decisions made in the real world setting that can be potentially generalized to become a new way to study the effects of AI on how clinicians make decisions. https://hai.stanford.edu/grants/
Gevaert and Gentles Featured in New PanCan Papers
Wanted: More Data, the Dirtier the Better
The computational immunologist Purvesh Khatri embraces messy data as a way to capture the messiness of disease. As a result, he’s making elusive genomic discoveries
To distill a clear message from growing piles of unruly genomics data, researchers often turn to meta analysis a tried and true statistical procedure for combining data from multiple studies.
How should an algorithm generate recommendations for patient care?
Dr. Jonathan Chen and his colleagues are working to answer the question, "How should an algorithm generate recommendations for patient care?" This question was tackled as the latest step in Dr. Chen’s quest to build OrderRex, a tool that will mine data from electronic health records to inform medical decisions.
Stem Cell Discoveries
The Gevaert lab discovered two types of intestinal stem cells using bioinformatics analysis of transcriptome data of several populations of intestinal stem cells. This work was a highly collaborative effort in the context of a national network of intestinal stem cell researchers.
Four image feature model predicts the presence of EGFR mutations in non small cell lung cancer
The Gevaert lab together with the department of radiology developed a model based on only four image features that predicts the presence of EGFR mutations in non small cell lung cancer. This work shows that macroscopic phenotypes such as tumor shapes, textures and tumor environment reflect their molecular properties.