Dr. Baranzini’s current research involves large genomic studies in MS patients to characterize the overall risk and activity of genes during different stages of the disease, differential response to treatment, and disease progression. His research also involves immunological studies using the EAE model, sequencing of whole genomes and transcriptomes from patients with MS and developing bioinformatics tools to integrate this information with that coming from other high throughput technologies. In addition, Dr. Baranzini collaborates with several interdisciplinary teams worldwide to computationally integrate all the available knowledge obtained in different research domains. He also leads the iMSMS, an international consortium to study the effect of bacterial populations (microbiota) on MS susceptibility and progression. Dr. Baranzini earned his degrees in clinical biochemistry (1992) and PhD in human molecular genetics (1997) from the University of Buenos Aires, Argentina.
Merging Electronic Health Records with a knowledge graph to improve predictive medicine
This talk will describe current efforts by UCSF, Google, ISB and LLNL in creating an open knowledge graph that can be used to improve AI-based predictions on EHR data.
Data science in combination with new tools help predict which patients will benefit most from health care interventions. Session contributors are representatives from medical organizations discussing various data science applications and their approaches to using data and predictive modeling to analyze and identify meaningful patterns that result in better patient outcomes.