Patient Subtyping via Time-Aware LSTM Networks

@article{Baytas2017PatientSV,
  title={Patient Subtyping via Time-Aware LSTM Networks},
  author={Inci M. Baytas and C. Xiao and Xi Zhang and Fei Wang and Anil K. Jain and Jiayu Zhou},
  journal={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2017}
}
  • Inci M. Baytas, C. Xiao, +3 authors Jiayu Zhou
  • Published 2017
  • Computer Science
  • Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  • In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. [...] Key Method The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit…Expand Abstract
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