What once may have seemed like a scene from a 22nd century sci-fi movie is reality today. High-speed, big data processing computers combine artificial intelligence with human know-how to crack complex health care conditions. This deep computer analysis may unveil new patterns that could bolster your health care provider’s ability to prescribe precise therapies, make a diagnosis, recommend a clinical trial or even predict your risk of disease.
Mayo Clinic Center for Individualized Medicine (CIM) is collaborating with the Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana Champaign (UIUC) to unleash the potential of artificial intelligence in patient care. Funded by a National Science Foundation (NSF) grant, the Mayo UIUC Alliance and corporate partners are conducting research into the big data challenge: how to develop computer systems that, combined with human intelligence, unlock new analysis of health and disease.
Identifying depression therapy
Research within CIM is probing whether artificial intelligence can reduce or eliminate the trial and error of prescribing antidepressant medication. In a clinical study with Mayo’s Department of Psychiatry, researchers have combined machine learning ─ a type of artificial intelligence ─with genomics, metabolomics and other clinical variables. This machine learning approach helps health care providers to choose a therapy most likely to work on the first try.
“We combined expertise from clinicians, engineers and biologists to create an algorithm that uncovered patterns of antidepressant response that each of these specialists alone might not be able to recognize,” says Arjun Athreya, a Mayo- UIUC Alliance predoctoral research fellow. “Using this data with deep machine learning, we were able to predict with 75-85 percent accuracy whether a common antidepressant drug would work for each individual patient in the study. That compares to 58 percent accuracy when predictions are based only on clinical, demographic and social factors. In addition, we found that women and men respond differently to this antidepressant therapy.”
“This example shows the power of machine-based learning and methodology,” says Liewei Wang, M.D., Ph.D, co-principal investigator on the NSF grant. “This innovative approach uses psychiatric assessments, biological and molecular data and genetic traits in a predictive model. That is different than trying to uncover patterns using traditional sociodemographic variables, which are known to be weaker predictors. It tells us that a machine learning, artificial intelligence approach holds promise for identifying personalized therapies to address unmet patient needs.”
The research brings together massive amounts of genomic data and patient health that providers may also use to predict:
- Whether a patient is at risk for developing disease
- Whether a gene might influence how a drug works
“Using these machine learning approaches, instead of saying a drug is going to be effective 50 percent of the time for the average population, we can say, ‘Based on this person’s genetic and molecular makeup, here is what is going to be most effective for that individual,’” says Ravishankar Iyer, Ph.D., of UIUC and co-principal investigator for the NSF grant. “These are methods that are pushing the boundaries of engineering and at the same time pushing the boundaries of medicine.”