Unsupervised characterization of Major Depressive Disorder medication treatment pathways

Learning health systems have the ability to systematically evaluate treatments and treatment pathways. Characterization of treatment pathways can enhance a health system's ability to perform systematic evaluation to improve care quality. In this study we use a Long-Short Term Memory (LSTM) autoencoder model to systematically characterize treatment pathways in a prevalent phenotype-Major Depressive Disorder (MDD). LSTM autoencoder models generate representations of medication treatment pathways that account for temporality and complex interactions. Patients with similar pathways are grouped with K-means clustering. Clusters are characterized by analysis of medication utilization sequences and trends, as well as clinical features, such as demographics, outcomes and comorbidities. Cluster characterization identifies endotypes of MDD including acute MDD, moderate-chronic MDD and severe-chronic, but managed MDD.