Machine learning predicts patients in need of advanced depression care

Using data from a statewide health information exchange, researchers have created machine learning algorithms that are able to identify patients who need advanced treatment for depression. According to Regenstrief Institute and Indiana University researchers, identifying cases of depression that require advanced care can be challenging for primary care physicians. However, they contend that their models--which leverage diagnostic, behavioral and demographic data, as well as past visit history from an HIE--can help PCPs predict which patients may be more at risk for adverse events from depression. Researchers created models for the entire patient population at Eskenazi Health, the public safety net healthcare system for Marion County, Indiana, as well as several different high-risk patient populations. "This study demonstrates the ability to automate screening for patients in need of advanced care for depression across an overall patient population or various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors and past visit history," conclude researchers in a recent article published in the Journal of Medical Internet Research.