Jun Guo

TL;DR: A deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission and is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness.

TL;DR: This paper employs the K-means clustering method to group users based on their trajectory information similarity and learn a shared RL policy for each group, which can achieve clear gains over the state-of-the-art RL methods for mHealth.

TL;DR: It is proved that the proposed algorithm can sufficiently decrease the objective function value at each iteration and will converge after a finite number of iterations, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.

TL;DR: In this article, the actor-critic contextual bandit (ACB) algorithm was proposed to reduce the influence of outliers while seeking for a similar solution compared with the state-of-the-art contextual bandsit methods on the datasets without outliers.