The document discusses using de-identified insurance claims data from 2014 to build a machine learning model to identify patients at risk of major depression. Specifically, it used data from approximately 2.5 million patients, including 48,000 with diagnoses of major depression and 2 million without mental health issues, to train a logistic regression model using 887 diagnostic codes and total claim numbers to assign probabilities of depression. The goal is to help identify depression in patients with chronic illnesses to improve outcomes and reduce costs.