Skip to main content
Open Access Publications from the University of California

UC Riverside

UC Riverside Electronic Theses and Dissertations bannerUC Riverside

Marked Temporal Point Processes for Irregular Time Series in the Context of Medical Data and Recommendation Systems

Creative Commons 'BY' version 4.0 license

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


Main Content
For improved accessibility of PDF content, download the file to your device.
Current View