BIDS-BCHSI Research Xchange Forum — Special Kickoff Event
Date: Monday, November 2, 2020
Time: 1:30-4:00 PM Pacific Time
Location: Virtual Participation
The BIDS-BCHSI Research Xchange Forum will kick off with this special event on November 2. Participants will be introduced to the Innovate For Health (I4H) Data Science Health Innovation Fellowship, including the 2019-2021 and 2020-2022 I4H Fellows; and hear about how research teams at UC Berkeley, UCSF, and Johnson & Johnson created this program to bring together experienced data scientists with academic and industry researchers to tackle challenging problems in healthcare through data-driven approaches.
1:30-1:40 PM — Welcome and I4H Introduction — I4H Co-Directors
— Emma Huang, Johnson & Johnson Innovation
— Karla Lindquist, UCSF
— Maryam Vareth, UC Berkeley and UCSF
1:40-1:50 PM — BIDS Introduction — David Mongeau, BIDS Executive Director
1:50-2:00 PM — BCHSI Introduction — Atul Butte, BCHSI Director
2:00-2:30 PM — New Fellows Introductions — 2020-2022 I4H Fellows
2:30-3:00 PM — Lightning Talks — 2019-2021 I4H Fellows
— Algorithmic Stewardship — Steph Eaneff, 2019-2021 I4H Fellow
— Using Biomedical Knowledge Graphs for Medical Diagnosis Prediction — Lowry Kirkby, 2019-2021 I4H Fellow
— Social Medicine Data Storytelling & Clinician Burnout — Laurens Kraal, 2019-2021 I4H Fellow
3:00-3:45 PM — Research Talk
— A consolidated framework for generating and validating synthetic, structured electronic health record data — Haley Hunter-Zinck, 2019-2021 I4H Fellow
Abstract: Synthetic data, data that mimics realistic patterns but does not correspond to actual data records, has the potential to provide high utility datasets while preserving privacy of contributors to real datasets. This concept is especially relevant to sensitive data such as patient electronic health records. Here, we use adapted generative adversarial networks to produce synthetic structured electronic health record data. We introduce data transformation procedures to generate categorical and numerical data simultaneously and review validation procedures for both realism and privacy preservation in the synthetic datasets. To improve usability, all preprocessing, training, generation, and validation is consolidated in an open-source software package. Finally, we demonstrate generalizability and utility of the approach by training synthetic data generators on three separate healthcare system datasets for the purpose of training predictive models for clinical outcomes with synthetic data.
3:45-4:00 PM — Closing Remarks — I4H Co-Directors
4:00-4:30 PM — Social/Networking (Optional)
The BIDS-BCHSI Research Xchange Forum is an open discussion platform for the interdisciplinary exchange of ideas and research projects at the intersection of healthcare and data science. Participants are invited to engage in a variety of activities, including presentations of work-in-progress, discussions and critiques of recent papers and AI methods in healthcare, introductions to new tools and methods, and opportunities to foster new collaborations. Invited speakers include leading voices in AI and Healthcare, and active conversations invite participants to share fresh perspectives. Clinicians and physicians with an interest in data science methods and tools, as well as data science faculty and researchers with applications or interests in the healthcare and health sciences, are welcome and encouraged to participate. Regular participants will also include the I4H Fellows, as well as post-docs, staff, and faculty from UC Berkeley, UCSF, and Johnson & Johnson. The immediate goals of this Forum are to share our current research projects with a wider audience, and to increase engagement and improve communication among the three host organizations. Meetings will be held virtually on the first Monday of each month at 12:30-1:30 PM Pacific Time, and interested members of the UC Berkeley, UCSF, and Johnson & Johnson communities are invited to use this registration form to receive the schedule and access links. Please contact InnovateForHealth@berkeley.edu for more information.
Maryam Vareth leads BIDS’ data science research efforts in the Health & Life Sciences. Dr. Vareth is a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced engineer, researcher, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open research and open source practices. Dr. Vareth received her BS and MS training in Electrical Engineering and Computer Science (EECS) from UC Berkeley, where she was awarded the prestigious Regent’s and Chancellor’s Scholarship. She completed her PhD through the joint UC Berkeley-UCSF Bioengineering program as a National Science Foundation Fellow, where she was awarded the Margaret Hart Surbeck Endowed Fellowship for Interdisciplinary Research for her work on developing new techniques and algorithms for the acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI), with the goal of improving its speed, sensitivity and specificity to improve the management of patients with brain tumors. She conducted her post-doctoral fellowship at UCSF, combining structural, physiological and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.
David Mongeau is the Executive Director of BIDS. With the Director and Faculty Council, he sets strategic direction and oversees the institute’s research, training, and outreach. David also leads the institute’s industry and foundation relations and its engagement with other UC and global research institutes – all toward the overarching mission at BIDS to create and deploy data science methods, practices, and technologies to enable discovery.
Previously, David co-led the data analytics institute at Ohio State; worked at Battelle, where he championed its proposal for an AI and cybersecurity company, now Covail; and worked for many years at Bell Labs – starting on the team that introduced the first C++ compiler and UNIX System V and leaving after building a global business and technology consulting practice, now part of Nokia Bell Labs Consulting.
David earned his undergraduate degree at Carnegie Mellon University, and later earned a graduate degree at Rensselaer Polytechnic Institute and an MBA from Purdue University. Many of his interests lie beyond data science, embracing the humanities and arts.
Haley Hunter-Zinck joined BIDS and BCHSI/UCSF in Fall 2019 as part of the first cohort of Data Science Health Innovation Fellows in the Innovate For Health program. She earned a Ph.D. in Computational Biology from Cornell University in 2014. She completed a postdoc in medical informatics at the VA Boston Healthcare System and continued at VA Boston between 2017 to 2019 as a researcher focusing on applying machine learning to emergency department and hospital flow problems.
Stephanie Eaneff is a data scientist with expertise at the intersection of public health and and public policy. She joined BIDS and BCHSI/UCSF in Fall 2019 as part of the first cohort of Data Science Health Innovation Fellows in the Innovate For Health program, having previously worked on a variety of interdisciplinary research teams including PatientsLikeMe, the New York Times machine learning team, Gryphon Scientific, and Talus Analytics. Steph holds a master’s degree in statistical practice from Carnegie Mellon University and a bachelor’s degree in biochemistry from UCLA.
Lowry Kirkby, now a computational neuroscientist with Rune Labs, joined BIDS and BCHSI/UCSF in Fall 2019 as part of the first cohort of Data Science Health Innovation Fellows in the Innovate For Health program. She obtained her undergraduate degree in Physics from the University of Oxford, and her PhD in Biophysics from the University of California, Berkeley. She completed her postdoctoral research in Neuroscience at the University of California, San Francisco, where she studied large-scale electrical recordings of the human brain to understand how neural circuits are altered in depression and anxiety.
Laurens Kraal joined BIDS and BCHSI/UCSF in Fall 2019 as part of the first cohort of Data Science Health Innovation Fellows in the Innovate For Health program. He received his Ph.D. in Bioinformatics from UCSF, during which he was also involved with multiple life-sciences startups. Most recently, Laurens held product and data roles at a microbiome diagnostics company. Laurens is passionate about making health data available, understandable, and actionable.