News & Announcements
Discover the latest research developments and scientific achievements of the BMIR team
The Shah Lab Green Button Team Awarded The 2019 ISP Star Award
The Shah Lab Green Button team (center, from left to right: Ken Jung, Vladimir Polony, Alison Callahan, Saurabh Gombar) received a 2019 ISP Star Award, which recognizes individuals from the Stanford Medicine community who, through their extraordinary efforts, embody the strategic priorities of our Integrated Strategic Plan (ISP): Value Focused, Digitally Driven, and Uniquely Stanford. By developing a technology that amplifies the clinical impact of our world-class faculty and creates outcome-driven recommendations at the point care, the Green Button exemplifies the transformative power of collaboration at Stanford Medicine. They were presented the award by David Entwistle (President and CEO of Stanford Health Care; left), Lloyd Minor (Dean of the School of Medicine; second from right) and Paul A. King (President and CEO of Stanford Children's Health; right).
Hernandez-Boussard and Colleagues Find that SSRIs Reduce the Effectiveness of Hydrocodone and Codeine
Research led by BMIR's Tina Hernandez-Boussard, colleague Ian Carroll and grad student Arjun Parthipan found that patients on SSRIs such as Zoloft, Paxil and Prozac who were prescribed prodrug opoids such as hydrocodone or codeine experienced more post-surgery pain than patients not on SSRIs. Hernandez-Boussard's team built a machine-learning algorithm that predicts how a patient will respond to different types of opoids.
Gevaert and Lucence Dx Team Up to Apply AI to Liver Cancer Diagnosis and Treatment
BMIR’s Olivier Gevaert and Lucence Diagnostics have teamed up to develop artificial intelligence algorithms for improving diagnosis and treatment of liver cancer with the goal of combining imaging and molecular data from liver cancer patients into smarter software tools that help physicians make better treatment decisions.
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.