Daniel Fabbri will serve as the principal investigator of a new 2-year UH1 grant from the Big Data to Knowledge program from the National Institutes Health to investigate crowdsourcing solutions for generating a wide range of labeled data sets from electronic health records (EHRs). This pilot project will solicit contributions from VUMC medical students and clinical personnel (nurses, residents and fellows), paying them to label EHR data.
More details on this grant can found here.
Thomas Lasko will serve as the principal investigator of a new 3-year R01 grant from the National Institute of Biomedical Imaging and Bioengineering. The goal of this project is to use cutting-edge methods from the data science and big data communities to provide rapidly interpretable visualizations of complex clinical data patterns that allow clinicians to quickly answer selected clinical questions that they face many times a day. This project aims to support the cognitive tasks involved in answering the following broad clinical questions: 1) What is the preoperative clinical status of this patient? 2) What are the common anesthetic approaches for this surgical procedure? And 3) What is the acuity level and complexity of each patient in the population of those who will be operated on tomorrow? These specific questions were selected from the clinical domain of anesthesia because that domain has fairly consistent practices between institutions, but we intend for our solutions to be easily extendable to analogous questions across clinical specialties. This project includes developing web-based tools that clinicians can use to answer these questions during their daily clinical practice.
More details on this grant can found here.
Beginning in Fall 2016, Vanderbilt University’s Big Biomedical Data Science (BIDS) Training Program will 1) provide matriculating PhD students with access to a diverse array of real big biomedical data sets, software tools, and applications at Vanderbilt (and interdisciplinary collaborations) and 2) integrate courses and faculty from across the institution to ensure that students are well-versed in the foundational competencies of computation, statistics, and biomedical science that are necessary to achieve reproducible success in this field. The program has been formed as a new Data Science Track within the existing Vanderbilt Biomedical Informatics PhD program. Thanks to a grant from the National Institutes of Health Big Data to Knowledge (BD2K) program, funding for accepted applicants includes tuition, stipend, health insurance, and travel allowance for up to five years, is available for eligible candidates. For more information on how to apply, please visit:
Jeffrey Blume, Ph.D. Associate Professor of Biostatistics
Cynthia Gadd, Ph.D. Professor of Biomedical Informatics
Bradley Malin, Ph.D., Associate Professor of Biomedical Informatics and Computer Science
Curriculum Overview of the Data Science Track of the BMI PhD Program
A key aspect of our training philosophy is that students need to be exposed to a variety of real world research applications and innovations along the big data spectrum while they are setting their methodological foundation in biomedical informatics, computer science, and statistics. Vanderbilt has an outstanding environment for this, as big data paradigms are being used in a wide range of interesting biomedical and health policy applications, e.g. *omics analysis, protein functioning based on structural biology, the development of decision support tools for clinicians based on EMR data, the development of precision medicine regimens. This fully integrated philosophy effectively marries real world applications of big data methods with their foundations, making the governing principles of data science - namely generalizability, reproducibility, and validity - much less abstract. A welcome by product of this is that students are often encouraged to contribute to projects by building tools that often end up being widely used for similar applications.
The BIDS PhD program is organized to provide all matriculating students with a shared core curriculum that is split across four areas. 1) Biomedical Informatics - courses in the foundations of clinical informatics and bioinformatics and the methods that support them; 2) Computer Science - courses in data structures, algorithms, machine learning, and big data infrastructure, 3) Statistical Methods - courses in biostatistics that follow a progression of basic principles to regression analysis and modeling and conclude with statistical inference methodologies, and 4) Biomedical Science - courses in the School of Nursing (for students focused on modeling, managing, and analyzing data from the clinical domain and studying clinical workflow) and the general biomedical graduate school program, which covers a wide range of topics from biochemistry to immunology.