Augmenting Transformer Autoencoders with Phenotype Classification for Robust Detection of Psychotic Relapses

Recently, deep autoencoder architectures have received attention for the problem of unsupervised anomaly detection. Detecting psychotic relapses in mental health patients is a crucial challenge, often framed as anomaly detection, given the limited availability of data during relapsing states. In this paper, motivated by the fact that during relapses patients tend to undergo behavioral changes, we augment the classical autoencoder architecture with extra patient identification components. We show that formulating the problem as one of both signal reconstruction and patient identification largely improves the overall precision and robustness of relapse detection and significantly outperforms previous methods with a relative improvement of 15%. In addition, we also explore multiple ways to fuse the identification and reconstruction errors into a unified anomaly score that outperforms the results achieved by each error in isolation.