New machine learning models are capable of predicting which patients might need more treatment for their depression than what their primary care provider can offer, reports a new study. The findings of the study are published in the Journal of Medical Internet Research. The algorithms were specifically designed to provide information the clinician can act on and fit into existing clinical workflows.
‘Novel machine learning model can help decrease the number of people who experience depressive symptoms that could potentially lead to suicide.
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Depression is the most commonly occurring mental illness in the world. The World Health Organization estimates that it affects about 350 million people. Some people may be able to manage their depression on their own or with guidance from a primary care provider. However, others may have more severe depression that requires advanced care from mental health care providers.Read More..
The Regenstrief and IU researchers created algorithms to identify those patients so that primary care doctors and providers can refer them to mental health specialists.
"Our goal was to build reproducible models that fit into clinical workflows," said Suranga N. Kasthurirathne, Ph.D., first author of the paper and research scientist at Regenstrief Institute. "This algorithm is unique because it provides actionable information to clinicians, helping them to identify which patients may be more at risk for adverse events from depression."
The algorithms combined a wide variety of behavioral and clinical information from the Indiana Network for Patient Care, a statewide health information exchange, for patients at Eskenazi Health. Dr. Kasthurirathne and his team developed algorithms for the entire patient population, as well as several different high-risk groups.
"By creating models for different patient populations, we offer health system leaders the option of selecting the best screening approach for their needs," said Dr. Kasthurirathne. "Perhaps they don't have the computational or human resources to run models on every single patient. This gives them the option to screen select high-risk patients." Dr. Kasthurirathne is also a visiting research assistant professor at the Indiana University Richard M. Fairbanks School of Public Health at IUPUI.
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Researchers are now working to integrate social determinants of health into these models.
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Source-Eurekalert