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.
Blood Test May Help Predict Severity of Dengue
BMIR’s Purvesh Khatri and colleague, Shirit Einav, identified 20 genes that can predict an individual’s likelihood of developing a severe form of dengue fever with about 80 percent accuracy. Using their newly identified set of genes as a foundation, Khatri and Einav aim to identify predictive biomarkers that can help doctors reliably gauge the likelihood of severe dengue the likelihood of severe dengue in patients who are newly symptomatic and use that information to provide more accurate care to help guide therapeutic clinical studies and, in the future, to guide treatment decisions.
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.
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