Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly ranking tasks, including contrastive one-class feature ranking and deviation ranking. The auxiliary tasks address two key challenges of the problem: 1) the high within class variance of depression samples: they enable the learning of representations that are robust to highly variant in-class distribution of the depression samples; and 2) the small labeled data volume: they significantly enhance the sample efficiency of the prediction model, which reduces the reliance on large depression-labeled datasets that are difficult to collect in practice. Extensive empirical results on large-scale child depression data show that our model is sample-efficient and can accurately predict depression 2–4 years before the illness occurs, substantially outperforming eight representative comparators.

Keywords

Anomaly detection, Deep learning, Depression prediction, One-class classification

Discipline

Databases and Information Systems | Longitudinal Data Analysis and Time Series

Research Areas

Data Science and Engineering

Publication

Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference PAKDD 2022, Chengdu, China, May 16-19: Proceedings

Volume

13281

First Page

236

Last Page

248

ISBN

9783031059353

Identifier

10.1007/978-3-031-05936-0_19

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-031-05936-0_19

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