Recurrent disease progression networks for modelling risk trajectory of heart failure
Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-dimensional data. However, very few studies have explored the ab...
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| Published in: | PloS one Vol. 16; no. 1; p. e0245177 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Public Library of Science
06.01.2021
Public Library of Science (PLoS) |
| Subjects: | |
| ISSN: | 1932-6203, 1932-6203 |
| Online Access: | Get full text |
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