Medical activities recommendation is a key aspect of an intelligent healthcare system, which can assist doctors with little clinical experience in clinical decision making. Medical activities recommendation can be seen as a kind of temporal set prediction. Previous studies about them are based on Recurrent Neural Network (RNN), which does not incorporate personalized medical history or differentiate between the impact of medical activities. To address the above-given issues, this paper proposes a Next-Day Medical Activities Recommendation (NDMARec) model. Specifically, our model firstly proposes an inpatient day embedding method based on soft-attention which balances the impact of different medical activities to get a joint representation of medical activities that occurred within the same day. Then, a fusion module is designed to combine features of inpatient day and medical history to achieve personalization. These features are learned by the self-attention mechanism that solves the long-term dependency problem of RNNs. Last, adversarial training is introduced to improve the generalization ability of our model. Extensive experiments on a real dataset from a hospital are conducted to show that NDMARec outperformed both classical and state-of-the-art methods.
Case-workload estimation has always been a complex process and plays a vital role in prosecutorial work. Despite the increasing development of rule-based techniques, artificial intelligence and machine learning have rarely been used to study case-workload estimation problems, leaving many cases processed without quantitative estimation. This paper aims to develop a new case-work estimation method that combines artificial intelligence methods with practical needs and apply it to the case assignment system of the prosecutor’s office. We propose a feature learning model, the improved AdaBoost model, to capture the features of cases for case grouping to estimate case workload. We first learn the case textual data based on the judicial proper noun dictionary, extract the case labels from the case information with the AdaBoost learner, and group and encode each case by fuzzy matching. Then, the extracted vital information estimates case workload based on the length of case processing time and suspects number, respectively. We conducted extensive experiments to compare the proposed method with eight baseline methods, including the traditional AdaBoost classifier, to evaluate the performance of the proposed model on a real prosecution case dataset. The experimental results demonstrate the superiority of our proposed workload estimation model.
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