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Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studies have embraced the multitask learning approach in the healthcare dom
Externí odkaz:
http://arxiv.org/abs/2406.11928
Autor:
Ning, Xuying, Xu, Wujiang, Liu, Xiaolei, Ha, Mingming, Ma, Qiongxu, Li, Youru, Chen, Linxun, Zhang, Yongfeng
Cross-Domain Sequential Recommendation (CDSR) methods aim to address the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR methods typically rely on overlapping users, designing complex cro
Externí odkaz:
http://arxiv.org/abs/2405.20710
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since real-world grap
Externí odkaz:
http://arxiv.org/abs/2212.03654
Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied in healthcare and finance. In recent years, deep learning models, esp
Externí odkaz:
http://arxiv.org/abs/2211.07956
Publikováno v:
In Expert Systems With Applications 30 November 2023 231
Publikováno v:
In Computers in Biology and Medicine September 2023 163
Publikováno v:
In Computers in Biology and Medicine February 2023 153
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Akademický článek
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Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its abilit
Externí odkaz:
http://arxiv.org/abs/1811.03760