Context-aware imputation for clinical time series

Kejing Yin, Kwok Wai CHEUNG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Missing data has been widely recognized as a key challenge of clinical time series analysis, which hinders the practical application of data-driven approaches to clinical data analytics [1], [2]. Various methods have been proposed to perform the time series imputation to alleviate this issue, yet most of them impose strong assumptions on the missing data, for instance, locality in Gaussian Process based models [3], lowrankness and temporal regularity in matrix/tensor factorization models [4], etc. More recently, researchers proposed to apply the Recurrent Neural Networks (RNNs) to tackle the missing data imputation problem for time series, where the RNNs try to capture and summarize the temporal dynamics using hidden state vectors [5]-[7].

Original languageEnglish
Title of host publication2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538691380
DOIs
Publication statusPublished - Jun 2019
Event7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference7th IEEE International Conference on Healthcare Informatics, ICHI 2019
Country/TerritoryChina
CityXi'an
Period10/06/1913/06/19

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Science Applications
  • Health Informatics
  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Context-aware imputation for clinical time series'. Together they form a unique fingerprint.

Cite this