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Precise prescription of medication dosing is crucial to patients, especially among Intensive Care Unit (ICU) patients. However, improper administration of some sensitive therapeutic medications (e.g., heparin) might place patients at unneeded risk, even life-threatening. Numerous factors such as a patient's clinical phenotype, genotype, and environmental factors will affect the heparin dose response. As a result, it is challenging to prescribe the optimal initial dose of heparin. In this paper, an individualized dosing policy is proposed to determine the optimal initial dose and minimize the risk of mis-dosing, as well as preventing the patients from late complications associated with medications dosing. A latent batch-constrained deep reinforcement learning (RL) algorithm is proposed to guarantee the safety of the medication recommendation system. The agent can observe a latent representation of patents and generate medication dosing solutions in successive and limited action spaces. The individualized dosing policy aims to reduce the extrapolation errors in the off-policy algorithms, by evaluating over-dosing and under-dosing of heparin in patients. Our results evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) database demonstrate that the latent batch-constrained RL algorithm can work effectively from the retrospective data, showing promise to be used in future medication dosing policies. [ABSTRACT FROM AUTHOR]

Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria recommender systems proved to have more accurate recommendations compared to single-criterion recommender systems as multi-criteria rating reflects the user appreciation of an item in terms of many aspects. On the another hand, deep learning techniques achieve promising performance in many research areas such as image processing, computer vision, pattern recognition and natural language processing. Recently, the application of deep learning in recommender systems have been frequently explored with encouraging results. Accordingly, this paper proposes a deep learning based algorithm for multi-criteria recommender systems in which deep autoencoders are employed to exploit the non-trivial, nonlinear and hidden relations between users with regard to multi-criteria preferences, and generate more accurate recommendations. Experiments on the Yahoo! Movies and TripAdvisor multi-criteria datasets show that the proposed algorithm prove to be very effective in terms of producing more accurate predictions compared with the state-of-the-art recommendation algorithms [ABSTRACT FROM AUTHOR]

Reinforcement learning techniques have recently been introduced to interactive recommender systems to capture the dynamic patterns of user behavior during the interaction with recommender systems and perform planning to optimize long-term performance. Most existing research work focuses on designing policy and learning algorithms of the recommender agent but seldom cares about the state representation of the environment, which is indeed essential for the recommendation decision making. In this paper, we first formulate the interactive recommender system problem with a deep reinforcement learning recommendation framework. Within this framework, we then carefully design four state representation schemes for learning the recommendation policy. Inspired by recent advances in feature interaction modeling in user response prediction, we discover that explicitly modeling user–item interactions in state representation can largely help the recommendation policy perform effective reinforcement learning. Extensive experiments on four real-world datasets are conducted under both the offline and simulated online evaluation settings. The experimental results demonstrate the proposed state representation schemes lead to better performance over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]