We are honored to announce our keynote speaker list for ICHI 2019.
Suzanne Bakken, R.N., Ph.D., FAAN, FACMI, FIAHSI, is the Alumni Professor of Nursing and a professor of biomedical informatics at Columbia University and Co-chair of the Health Analytics Center of the Data Science Institute. Following doctoral study in nursing at the University of California, San Francisco, she completed a National Library of Medicine postdoctoral fellowship in medical informatics at Stanford University. The goal of Dr. Bakken's program of research is to promote health and reduce health disparities in underserved populations through the application of innovative informatics and data science methods. A major focus of her current grant portfolio is the visualization of health care data for community members, patients, clinicians, and community-based organizations. Dr. Bakken currently directs the Precision in Symptom Self-Management Center and the Reducing Health Disparities Through Informatics pre-doctoral and postdoctoral training program, both funded by the National Institute of Nursing Research. She also served as the principal investigator of the Agency for Healthcare Research and Quality-funded Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) and its follow-up study, WICER 4 U, which is focused on promoting the use of WICER infrastructure through stakeholder engagement, including the return of individual research results. She has also received funding from the National Cancer Institute, National Library of Medicine, National Institute of Mental Health, and the Health Resources and Services Administration. Dr. Bakken has published more than 300 peer-reviewed papers and is Editor-in-Chief of the Journal of the American Medical Informatics Association. In 2010 she received the Pathfinder Award from the Friends of the National Institute of Nursing Research and was inducted into the Sigma Theta Tau International Nurse Research Hall of Fame in 2018. Dr. Bakken is a fellow of the New York Academy of Medicine, American Academy of Nursing, American College of Medical Informatics, International Academy of Health Sciences Informatics, and a member of the National Academy of Medicine.
There are multiple drivers at the societal level to share individual results with research participants. These include an increase in citizen science, the implementation of All of Us® and other precision medicine initiatives, and research participant demands. Recommendations from a recent National Academy of Sciences report delineate under what circumstances and in what manner biomarker research results including genomic results should be shared. It is not possible to share individual research results at scale without the use of effective communication practices and an information and data science infrastructure. This talk will have five main components: (1) societal drivers for sharing of research results, (2) recommendations from the National Academy of Science report, (3) information and data science infrastructure, and (4) examples of sharing individual research results, and (5) implications for health informatics.
Director, National Library of Medicine
Dr. Brennan is the Director of the National Library of Medicine (NLM) and Adjunct Investigator in the National Institute of Nursing Research’s Advanced Visualization Branch at the National Institutes of Health (NIH). As the world’s largest biomedical library, NLM produces digital information resources used by scientists, health professionals, and members of the public.
Prior to joining NLM, Dr. Brennan was the Lillian L. Moehlman Bascom Professor at the School of Nursing and College of Engineering at the University of Wisconsin–Madison. Dr. Brennan is a pioneer in the development of innovative information systems and services such as ComputerLink, an electronic network designed to reduce isolation and improve self-care among home care patients. She directed HeartCare, a web-based information and communication service that helps home-dwelling cardiac patients recover faster and with fewer symptoms, and also directed Project HealthDesign, an initiative designed to stimulate the next generation of personal health records.
A past president of the American Medical Informatics Association, Dr. Brennan was elected to the National Academy of Medicine in 2001. She is a fellow of the American Academy of Nursing, the American College of Medical Informatics, and the New York Academy of Medicine.
Academician of Chinese Academy of Engineering
Chinese Academy of Medical Sciences/Peking Union Medical College, China
Prof. Depei Liu was elected as the Academician of National Academy of Medicine (NAM), Academician of Third World Academy of Sciences (TWAS) and Academician of European Academy of Sciences. Prof. Liu conducted “National Scientific Data Sharing Platform for Population and Health”, prompting scientific data sharing and data-driven medical sciences.
Andrey Rzhetsky is an Edna K. Papazian Professor of Medicine and Human Genetics, and a co-Chief of Section of Computational Biomedicine and Biomedical Data Science at the Department of Medicine at the University of Chicago. He is also a Pritzker Scholar, and a Senior Fellow at the Institute for Genomics and Systems Biology at the University of Chicago. His research is focused on computational dissection of etiology of complex human diseases.
I will attempt to cover several interrelated analysis topics, spending more time on parts that resonate with the audience.
First, I will introduce our recent study analyzing phenotypic data harvested from over 150 million unique patients. Curiously, these non-genetic large-scale data can be used for genetic inferences. We discovered that complex diseases are associated with unique sets of rare Mendelian variants, referred to as the “Mendelian code.” We found that the genetic loci indicated by this code were enriched for common risk alleles. Moreover, we used probabilistic modeling to demonstrate for the first time that deleterious Mendelian variants likely contribute to complex disease risk in a non-additive fashion.
The second topic that I hope to cover is analysis of apparent clusters of neurodevelopmental disorders. Disease clusters are defined as geographically compact areas where a particular disease, such as a cancer, shows a significantly increased rate. It is presently unclear how common are such clusters for neurodevelopmental maladies, such as autism spectrum disorders (ASD) and intellectual disability (ID). As in the first story, examining data for one third of the whole US population, we demonstrated that (1) ASD and ID are manifesting strong clustering across US counties; (2) counties with high ASD rates also appear to have high ID rates, and (3) the spatial variation of both phenotypes appears to be driven by environment, and, by a lesser extent, by economic incentives at the state level.
The third topic is about using electronic medical record data to 1) estimate the heritability and familial environmental patterns of diseases, and 2) infer the genetic and environmental correlations between disease pairs from a set of complex diseases. I am particularly interested in inferring objective classifications of diseases (based on a formal optimization criterion), separately from environmental and genetic factors.