Knowledge tracing is the task of understanding student’s knowledge acquisition processes by estimating whether to solve the next question correctly or not. Most deep learning-based methods tackle this problem by identifying hidden representations of knowledge states from learning histories. However, due to the sparse interactions between students and questions, the hidden representations can be easily over-fitted and often fail to capture student’s knowledge states accurately. This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships. To deal with the complexity of knowledge acquisition during learning, we carefully design the components of contrastive learning, such as architectures, data augmentation methods, and hard negatives, taking into account pedagogical rationales. Our extensive experiments on six benchmarks show statistically significant improvements from the previous methods. Further analysis shows how our methods contribute to improving knowledge tracing performances.

Predicting the clinical outcome of patients from the historical electronic health records (EHRs) is a fundamental research area in medical informatics. Although EHRs contain various records associated with each patient, the existing work mainly dealt with the diagnosis codes by employing recurrent neural networks (RNNs) with a simple attention mechanism. This type of sequence modeling often ignores the heterogeneity of EHRs. In other words, it only considers historical diagnoses and does not incorporate patient demographics, which correspond to clinically essential context, into the sequence modeling. To address the issue, we aim at investigating the use of an attention mechanism that is tailored to medical context to predict a future diagnosis. We propose a medical context attention (MCA)-based RNN that is composed of an attention-based RNN and a conditional deep generative model. The novel attention mechanism utilizes the derived individual patient information from conditional variational autoencoders (CVAEs). The CVAE models a conditional distribution of patient embeddings and his/her demographics to provide the measurement of patient's phenotypic difference due to illness. Experimental results showed the effectiveness of the proposed model.

Recommender systems offer critical services in the age of mass information. A good recommender system selects a certain item for a specific user by recognizing why the user might like the item. This awareness implies that the system should model the background of the items and the users. This background modeling for recommendation is tackled through the various models of collaborative filtering with auxiliary information. This paper presents variational approaches for collaborative filtering to deal with auxiliary information. The proposed methods encompass variational autoencoders through augmenting structures to model the auxiliary information and to model the implicit user feedback. This augmentation includes the ladder network and the generative adversarial network to extract the low-dimensional representations influenced by the auxiliary information. These two augmentations are the first trial in the venue of the variational autoencoders, and we demonstrate their significant improvement on the performances in the applications of the collaborative filtering.

Understanding hospitals' relationships is critical to the analysis of public healthcare environment. There have been many attempts to analyze medical environment at a personal level. Recently, at an organizational level, there has been some advance in research into examining a relationship between hospitals. However, the formation of linkages is restricted to explicit and direct interactions. In contrast, we focused on implicit information flows between hospitals. This study also analyzes large scale hospital networks based on prescribing similarity. The sample dataset we used is the trustworthy representative of actual population in Korea. We assessed the impact of Drug Utilization Review (DUR) on hospital network characteristics. We examined National Inpatient Sample (NIS) dataset for before-DUR year (2010) and after-DUR year (2011). Various network metrics and performance measures are calculated for the two years. Generated hospital networks of the two years were significantly different in terms of both network metrics and performance measures, except for a riskiness measure. In network clustering result, Spearman's correlation coefficients indicated that network metrics can be used to evaluate hospitals having extreme prescription patterns. We anticipate our novel approach allows us to better understand public healthcare environment.

Analyzing patient records is important for improving the quality of medical services and for understanding each patient's historical diseases. However, the huge size of the data requires statistical analysis procedures. In this paper, we proposed a probabilistic model-the disease-medicine topic model (DMTM)-to explore connected knowledge about diseases and medicines. In the model, diseases and medicines are modeled using generative process. We used the latent Dirichlet allocation, which is one of the most popular topic models, as the baseline model. Then, we compared the qualities of topic representations quantitatively and qualitatively. The comparison results showed that the topics derived from the DMTM are clearer to identify and that specific patterns were found in the diseases and medicines. In the case of topic network analysis, these specific patterns were proved using centrality measurements.