M-SEQ: Early detection of anxiety and depression via temporal orders of diagnoses in electronic health data

According to a 2014 Spring American College Health Association Survey, almost 50% of college students reported feeling things were hopeless and that it was difficult to function within the last 12 months. More than 80% reported feeling overwhelmed and exhausted by their responsibilities. This critical subpopulation of Americans is facing significant levels of mental health disorders, challenging colleges to provide accessible and high quality behavioral health care. However, psychiatric disorders are frequently unrecognized in primary care settings, posing physical, emotional, economic, and social burdens to patients and others. Towards the goal of earlier identification and treatment of mental health disorders, this paper proposes M-SEQ, an early detection framework for anxiety/depression using electronic health data from primary care visit sequences. Specifically, compared to existing methods that predict a future disease state using frequency of diagnoses in a patient´s medical history, we hypothesize that future disease might also be correlated with the temporal orders of diagnoses. Thus, M-SEQ first discovers a set of diagnosis codes that are discriminative of anxiety/depression, and then extracts each diagnosis pair from each patient´s health record to represent the temporal orders of diagnoses. Further, it incorporates the extracted temporal order information with the existing representation to predict whether a patient is at risk of anxiety/depression. We evaluate M-SEQ using the electronic health record (EHR) data of 213,112 college students from 10 schools participating in the College Health Surveillance Network (CHSN) from January 1, 2011 through December 31, 2014. The experimental results shows that our framework can detect a future diagnosis of anxiety and depression based on the primary care visit data up to 3 months in advance, with approximately 1%-4.5% higher accuracy, compared to baseline methods using frequency of diagnoses.