Grayson Ruhl
Grayson Ruhl – received his BA in Computer Science from The University of the South (May 2017). Grayson was a summer intern here Summer 2015 and mentored by Brad Malin. Grayson is a PhD student funded from the BIDS training grant.
Grayson Ruhl – received his BA in Computer Science from The University of the South (May 2017). Grayson was a summer intern here Summer 2015 and mentored by Brad Malin. Grayson is a PhD student funded from the BIDS training grant.
Kimberley (Kim) Kondratieff – received MS in Statistics from San Jose State University (May 2016). Kim was a summer intern here Summer 2015 and mentored by Tom Lasko. Kim is a PhD student funded from the BIDS training grant.
Dr. Alvin Jeffery is an Assistant Professor of Biomedical Informatics and Nursing at Vanderbilt University. He completed his PhD (Nursing Science & Health Services Research) at Vanderbilt University’s School of Nursing in 2017 and a Medical Informatics Post-Doctoral Fellowship with the U.S. Department of Veterans Affairs and Vanderbilt University’s Department of Biomedical Informatics in 2019. Dr. Jeffery previously held an AHRQ/PCORI K12 focused on Learning Healthcare Systems and implementation science. He is currently funded by: (1) an NIH/NIDA Avenir DP1 to develop precision phenotypes for substance use disorders with the aim of accelerating genetics studies and (2) the Betty Irene Moore Fellowship for Nurse Leaders and Innovators focused on customizing electronic health record systems for a wide range of users and settings. He has a background in pediatric critical care nursing and as a staff educator at Cincinnati Children’s Hospital Medical Center. He holds board certifications in pediatric critical care nursing and as a Family Nurse Practitioner. He is a former Emerging Leader with the Alliance of Nursing Informatics.
Expertise: Dr. Jeffery focuses on the design, development, and evaluation of probability-based clinical decision support tools. He leverages machine learning and data science techniques for developing prediction models with an emphasis on predicting outcomes that are incompletely ascertainable. He also incorporates qualitative methods for exploring how to implement CDS tools within clinicians’ cognitive and physical workflows. In addition to his scientific talks, he has delivered numerous presentations on pediatric critical care topics as well as leadership and professional development skills. He has written 4 books and more than 35 journal publications. For examples of Dr. Jeffery’s research products or to hear him talk about some of his work, you can explore his personal website. To discover resources for learning more about data science, you can explore his nursing data science website.
Other Links:
Bluesky: https://bsky.app/profile/nursealvin.bsky.social
Google Scholar
MyBibliography
Education & Training
MSCI, Vanderbilt University, Nashville, TN (2020)
Fellowship, University of Michigan, Ann Arbor, MI (2016)
Research Fellowship, University of Michigan, Ann Arbor, MI (2012)
Residency, University of Michigan, Ann Arbor, MI (2015)
MD, Baylor College of Medicine, Houston, TX (2010)
MS in Biotechnology, Johns Hopkins University, Baltimore, MD (2007)
BA in German Literature, Johns Hopkins University, Baltimore, MD (2005)
Daniel W. Byrne (Retired 1/21/24) worked as a biostatistician, artificial intelligence researcher, author, and faculty member in the Department of Biostatistics in the School of Medicine at Vanderbilt University, with secondary appointments in the Departments of Biomedical Informatics and Medicine. He was the Director of Artificial Intelligence Research, AVAIL (Advanced Vanderbilt Artificial Intelligence Laboratory). His AVAIL team tested in randomized controlled trials whether real-time predictive models embedded in the electronic health record (EHR) can be used to focus prevention and improve health outcomes.
He has received numerous teaching awards in the Vanderbilt MSCI (https://medschool.vanderbilt.edu/msci/) program - Biostatistics I and Medical Writing for Clinical Investigators. Previously he has also taught these courses in Japan.
He is the author of an award-winning book "Publishing Your Medical Research" (https://www.amazon.com/dp/1496353862) and more than 150 peer-reviewed medical research publications. His second book was published at the end of 2022: "Artificial Intelligence for Improved Patient Outcomes - Principles for Moving Forward with Rigorous Science".
He is an internationally recognized predictive analytics expert with over four decades experience in healthcare risk modeling to improve patient outcomes. His research lies at the intersection of next generation artificial intelligence in healthcare, machine learning, predictive modeling, big data analytics, precision medicine, and EHR data mining.
Prior to assuming his position at Vanderbilt in 1999, he was a self-employed biostatistical consultant (Byrne Research) in Ridgefield, CT for 10 years.
Daniel Byrne received his undergraduate degree from the State University of New York at Albany and his graduate degree from New York Medical College.
Research Information
For Google Scholar listing, click here. (https://scholar.google.com/citations?user=Eg4zVpYAAAAJ&hl=en)
For complete reference list, click here. (https://www.ncbi.nlm.nih.gov/pubmed/?term=Byrne-DW)
For homepage, click here. (https://www.vumc.org/biostatistics/person/daniel-w-byrne)
For ResearchGate listing, click here. (https://www.researchgate.net/profile/Daniel_Byrne)