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A wide variety of methods have been developed for identifying depression, butthey focus primarily on measuring the degree to which individuals are sufferingfrom depression currently. In this work we explore the possibility ofpredicting future depression using machine learning applied to longitudinalsocio-demographic data. In doing so we show that data such as housing status,and the details of the family environment, can provide cues for predictingfuture psychiatric disorders. To this end, we introduce a novel deep multi-taskrecurrent neural network to learn time-dependent depression cues. Thedepression prediction task is jointly optimized with two auxiliary anomalyranking tasks, including contrastive one-class feature ranking and deviationranking. The auxiliary tasks address two key challenges of the problem: 1) thehigh within class variance of depression samples: they enable the learning ofrepresentations that are robust to highly variant in-class distribution of thedepression samples; and 2) the small labeled data volume: they significantlyenhance the sample efficiency of the prediction model, which reduces thereliance on large depression-labeled datasets that are difficult to collect inpractice. Extensive empirical results on large-scale child depression data showthat our model is sample-efficient and can accurately predict depression 2-4years before the illness occurs, substantially outperforming eightrepresentative comparators.

Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification