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Found 4 papers, 1 papers with code
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data
Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood.
Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study
Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care.
Think Locally, Act Globally: Federated Learning with Local and Global Representations
To this end, we propose a new federated learning algorithm that jointly learns compact local representations on each device and a global model across all devices.
CLPsych2019 Shared Task: Predicting Suicide Risk Level from Reddit Posts on Multiple Forums
The NB model had the best performance in two additional binary-classification tasks, i. e., no risk vs. flagged risk (any risk level other than no risk) with F1 score 0. 836 and no or low risk vs. urgent risk (moderate or severe risk) with F1 score 0. 736.
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