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Multitask Learning for Mental Health Conditions with Limited Social Media Data

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Figure

Figure 1:STL model in plate notation (left):(right): shared weights trained jointly for all tasks,with task-specific hidden layers
Figure 2 shows the AUC-score of each model foreach task separately, and Figure 3 the true positiverate at a low false positive rate of 0.1
Figure 3: TPR at 0.10 FPR for different main tasks
Figure 4: ROC curves for predicting each condition. The precision (diagnosed, correctly labeled) is onthe y-axis, while the proportion of false alarms (control users mislabeled as diagnosed) is on the x-axis.Chance performance is indicated by the dotted diagonal line.
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