Investigating patterns in medical data through advanced machine learning
The Medical Machine Intelligence Lab investigates a broad range of questions, centered
on learning of patterns from information in the real world.
Headquartered at the University of Utah, we work closely with industry partners to uncover critical, impactful insights from data. If you have an
interesting problem that could benefit from collaboration with academic medical center, please reach out to the lab director.
The large language models powering modern AI have the potential to unlock similar advances in treating genetic conditions. We investigate the use of innovative genomic foundation models to uncover complex polygenetic interactions. In particular, we focus on predicting the progression of Alzheimer's dementia.
Health status changes over time, requiring models that detect complicated, noisy patterns. We develop novel architectures for detecting patterns in sequential medical data, such as telemetry or electronic health records. Applications include addiction, dementia, suicide prevention and maternal health.
While there is an ever-growing abundance of rich medical data, different formats and properties hinder its ability to advance medicine. We undertake large efforts to harmonize data across sources, optimizing data for use in machine learning.
Machines are incredible tools to aid in human decision making, and to gain insight into underlying cognitive mechanisms. We use computational models to investigate the properties of decision-making, how it can be optimized in medical practice, and how it can be altered in neuropsychiatric conditions.
Dr. Pettine is an Assistant Professor of Psychiatry at Huntsman Mental Health Institute. He is adjunct faculty in the Department of Biomedical Engineering and has affiliations with the Scientific Computing and Imaging Institute and the Interdisciplinary Neuroscience Program. He received computational neuroscience training through research positions at Stanford, NYU, and Yale.
Dr. Christenson is an Adjunct at the University of Utah, and Principal Machine Learning Scientist at MTN. He trained in computational neuroscience at University College London and Columbia University. Dr. Christenson has extensive industry experience in the application of machine intelligence to biomedicine.
Dr. Locke is an Assistant Professor of Pulmonary and Critical Care at Intermountain Health. He had data science training from the University of Colorado and the University of Utah. Dr. Locke is an expert in the application of machine intelligence to clinical decisions.
Dr. Koirala is an Adjunct Assistant Professor at the University of Maryland. He practiced as an expedition medicine physician in the Himalayas, and has extensive industry experience in medical machine learning.
Dr. Charles Kemmler is an Assistant Professor at the University of Pennsylvania. He has a Ph.D. and M.D. from the University of Colorado. He is an expert in the application of machine intelligence to clinical practice.
Ioanna Douka, M.D.
Co-chief Psychiatry Research Track, University of Utah
MD Rakibul Haque, Ph.D.
Research Fellow, Kahlert School of Computing, University of Utah
Sudhanva Manjunath Athreyat
Masters Student, Kahlert School of Computing, University of Utah
Andre Chu
Masters Student, Biomedical Informatics, University of Utah
Paul Rosen, Ph.D.
Associate Professor at the University of Utah in the Scientific Computing and Imaging Institute and Kahlert School of Computing
John D. Murray, Ph.D.
Associate Professor at the Dartmouth College in the Department of Psychology and Brain Science
Suma Jacob, MD, PhD
Professor, Director, Division of Child and Adolescent Psychiatry at UCLA
Shireen Elhabian, Ph.D.
Associate Professor at the University of Utah in the Scientific Computing and Imaging Institute and Kahlert School of Computing
Jorie Butler, Ph.D.
Associate Professor at the University of Utah Department of Biomedical Informatics and Division of Geriatrics in the Department of Internal Medicine
Hilary Coon, Ph.D.
Benning Endowed Presidential Professor with appointments in Psychiatry, Bioinformatics, Neurobiology, and Genetic Epidemiology
Anna Docherty, Ph.D.
Associate Professor in the University of Utah Department of Psychiatry
Vincent Kopplemans, Ph.D.
Research Associate Professor in the University of Utah Department of Psychiatry
Andrey Shabalin, Ph.D.
Research Assistant Professor in the University of Utah Department of Psychiatry
Vishwa Gouder, Ph.D.
Independent machine intelligence researcher
MTN
A system that leverages clinical and physiological data to provide intelligence for patient monitoring. The lab leadership holds a financial interest.
Lovu
A digital health platform that integrates technology, services, and data to enhance maternal outcomes by supporting expectant mothers, clinicians, and payers.
Videra Health
AI-driven mental health assessment platform that enables providers to proactively identify, triage, and monitor at-risk patients through video, text, and audio analyses.