Time-aware long short-term memory
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Time-aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference.[1] Experiments using real and synthetic data proved that T-LSTM auto-encoder outperformed widely used frameworks including LSTM and MF1-LSTM auto-encoders.[2]
References
[edit]- ↑ Baytas, Inci M.; Xiao, Cao; Zhang, Xi; Wang, Fei; Jain, Anil K.; Zhou, Jiayu (2017). "Patient Subtyping via Time-Aware LSTM Networks". Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 65–74. doi:10.1145/3097983.3097997.
- ↑ Baytas, Inci M.; Xiao, Cao; Zhang, Xi; Wang, Fei; Jain, Anil K.; Zhou, Jiayu (2017). "Patient Subtyping via Time-Aware LSTM Networks". Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 65–74. doi:10.1145/3097983.3097997.
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