A team of researchers from the Department of Veterans Affairs, Oak Ridge National Laboratory, Harvard’s T.H. Chan School of Public Health, Harvard Medical School and Brigham and Women’s Hospital has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

The method, called Knowledge Extraction via Sparse Embedding Regression, or KESER, was published recently in Nature Digital Medicine. The process integrates electronic health record data from two large institutions — the VA and Boston-based Partners Healthcare — and provides automated feature selection that leads to phenotype identification algorithms and knowledge discovery.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

“KESER provides a high-level view of the relationships between clinical knowledge that we can’t always see when caring for patients at the individual or group level,” said Dr. Katherine Liao, a principal investigator of KESER at VA Boston and associate professor of medicine at Harvard Medical School. “We look forward to translating the study’s methods and results from applications in clinical research to advancements in clinical care.”