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- W3122022143 endingPage "102021" @default.
- W3122022143 startingPage "102021" @default.
- W3122022143 abstract "In this work, we propose a novel autoregressive event time-series model that can predict future occurrences of multivariate clinical events. Our model represents multivariate event time-series using different temporal mechanisms aimed to fit different temporal characteristics of the time-series. In particular, information about distant past is modeled through the hidden state space defined by an LSTM-based model, information on recently observed clinical events is modeled through discriminative projections, and information about periodic (repeated) events is modeled using a special recurrent mechanism based on probability distributions of inter-event gaps compiled from past data. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that our new model equipped with the above temporal mechanisms leads to improved prediction performance compared to multiple baselines." @default.
- W3122022143 created "2021-02-01" @default.
- W3122022143 creator A5012461386 @default.
- W3122022143 creator A5053320499 @default.
- W3122022143 date "2021-02-01" @default.
- W3122022143 modified "2023-09-27" @default.
- W3122022143 title "Modeling multivariate clinical event time-series with recurrent temporal mechanisms" @default.
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- W3122022143 doi "https://doi.org/10.1016/j.artmed.2021.102021" @default.
- W3122022143 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7943294" @default.
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