Intelligent career planning via stochastic subsampling reinforcement learning.

Career planning consists of a series of decisions that will significantly impact one's life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one's career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one's career life.

RAHM: Relation augmented hierarchical multi-task learning framework for reasonable medication stocking.

As an important task in digital preventive healthcare management, especially in the secondary prevention stage, active medication stocking refers to the process of preparing necessary medications in advance according to the predicted disease progression of patients. However, predicting preventive or even life-saving medicine for each patient is a non-trivial task. Existing models usually overlook the implicit hierarchical relation between patient's predicted diseases and medications, and mainly focus on single tasks (medication recommendation or disease prediction). To tackle this limitation, we propose a relation augmented hierarchical multi-task learning framework, named RAHM. which is capable of learning multi-level relation-aware patient representation for reasonable medication stocking. Specifically, the framework first leverages the underlying structural relations of Electronic Health Record (EHR) data to learn the low-level patient visit representation. Then, it uses a regular LSTM to encode the historical temporal disease information for disease-level patient representation learning. Further, a relation-aware LSTM (R-LSTM) is proposed to handle the relations between diseases and medication in longitudinal patient records, which can better integrate the historical information into the medication-level patient representation. In the learning process, two pseudo residual structures are introduced to mitigate the error propagation and preserve the valuable relation information of EHRs. To validate our method, extensive experiments have been conducted based on the real-world clinical dataset. The results demonstrate a consistent superiority of our framework over several baselines in suggesting reasonable stock medication.