Patient Subtyping via Time-Aware LSTM Networks

In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse…

• NS
  National Science Foundation

  Award: IIS-1565596,IIS-1615597,IIS-1650723
• OO
  Office of Naval Research

  Award: N00014-17-1-2265