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.

通过应用深度多任务循环神经网络学习时间相关的抑郁线索，本文探讨了使用机器学习预测未来抑郁症的可能性，利用住房状况和家庭环境的细节，提供了预测未来精神障碍的线索，并使用对比一类特征排名和偏差排名两个辅助任务显着提高了预测模型的样本效率，从而减少了对大型抑郁症标记数据集的依赖。对大型儿童抑郁症数据的大量实证结果表明，我们的模型是样本高效的，能够在疾病发生前 2-4 年准确预测抑郁症，远远优于 8 个典型的比较模型。

基于联合异常排名和分类的纵向数据深度抑郁预测

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

In this work, we propose a multi-task recurrent neural network with attention
mechanism for predicting cardiovascular events from electronic health records
(EHRs) at different time horizons. The proposed approach is compared to a
standard clinical risk predictor (QRISK) and machine learning alternatives
using 5-year data from a NHS Foundation Trust. The proposed model outperforms
standard clinical risk scores in predicting stroke (AUC=0.85) and myocardial
infarction (AUC=0.89), considering the largest time horizon. Benefit of using
an \gls{mt} setting becomes visible for very short time horizons, which results
in an AUC increase between 2-6%. Further, we explored the importance of
individual features and attention weights in predicting cardiovascular events.
Our results indicate that the recurrent neural network approach benefits from
the hospital longitudinal information and demonstrates how machine learning
techniques can be applied to secondary care.

本研究提出了一种基于多任务循环神经网络和注意机制的方法，用于预测心血管事件。与标准的临床风险评估模型相比，该模型在预测中风和心肌梗死方面具有更好的性能，并且在短期内的预测效果可以通过使用多任务学习得到提升。同时，该研究还探讨了个体特征和注意力权重对于预测心血管事件的重要性，并证明了机器学习技术可以被应用于二级保健。