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With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients health records in clinics. While a search function can be a useful alternative to browsing through a patients record, it is cumbersome for clinicians to search repeatedly for the same or similar information on similar patients. Under such circumstances, there is a critical need to build effective recommender systems that can generate accurate search term recommendations for clinicians. In this manuscript, we developed a hybrid collaborative filtering model using patients encounter and search term information to recommend the next search terms for clinicians to retrieve important information fast in clinics. For each patient, the model will recommend terms that either have high co-occurrence frequencies with his/her most recent ICD codes or are highly relevant to the most recent search terms on this patient. We have conducted comprehensive experiments to evaluate the proposed model, and the experimental results demonstrate that our model can outperform all the state-of-the-art baseline methods for top-N search term recommendation on different datasets.
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict
Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant pro
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the wel
Collaborative Filtering (CF) is one of the most used methods for Recommender System. Because of the Bayesian nature and nonlinearity, deep generative models, e.g. Variational Autoencoder (VAE), have been applied into CF task, and have achieved great
With the increasing scale and diversification of interaction behaviors in E-commerce, more and more researchers pay attention to multi-behavior recommender systems that utilize interaction data of other auxiliary behaviors such as view and cart. To a