Marked Temporal Point Processes for Irregular Time Series in the Context of Medical Data and Recommendation Systems
- Author(s): Islam, Kazi
- Advisor(s): Shelton, Christian R
- et al.
Electronic Health Records (EHRs) consist of sparse, noisy, incomplete, heterogeneous, and
unevenly sampled clinical data of patients. Such data can be treated as a temporal stream
of events of varied types occurring at irregularly spaced time points. Our first approach
to model such data is a Bayesian structure learning based marked temporal point process
model. We model the event streams, including vital signs, laboratory results contained
in two different datasets using a piecewise-constant conditional intensity model (PCIM),
a type of marked point process. Next, we model the stream of discrete clinical events
in continuous time using a neurally self-modulating marked temporal point process model
that uses continuous-time long short-term memory (LSTM) cells as its building blocks.
Our methods improve prediction performance in multiple tasks, including in-hospital mortality prediction, while providing suitable regularization and bypassing the need for data