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- W4285601277 abstract "We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them." @default.
- W4285601277 created "2022-07-16" @default.
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- W4285601277 date "2022-07-01" @default.
- W4285601277 modified "2023-10-17" @default.
- W4285601277 title "Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules" @default.
- W4285601277 doi "https://doi.org/10.24963/ijcai.2022/536" @default.
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