Sequential Representation of Sparse Heterogeneous Data for Diabetes Risk Prediction.
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2023)
摘要
Type 2 diabetes (T2D) is a major public health problem, and opportunistic screening to detect T2D at an early stage can help initiate interventions that delay or prevent the disease and its complications. In this study, we use electronic health records (EHR) and concepts extracted from clinical notes to predict future T2D risk. Our deep neural network-based model captures the temporal sequence of patient visits. We use explainable AI algorithms to assess the model decisions and observe alignment with the domain knowledge of clinical experts.
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关键词
Machine Learning,Natural Language Processing,Disease Prediction,Diabetes
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