Context-Aware Time Series Imputation for Multi-Analyte Clinical Data

Kejing Yin*, Liaoliao Feng, Kwok Wai CHEUNG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Clinical time series imputation is recognized as an essential task in clinical data analytics. Most models rely either on strong assumptions regarding the underlying data-generation process or on preservation of only local properties without effective consideration of global dependencies. To advance the state of the art in clinical time series imputation, we participated in the 2019 ICHI Data Analytics Challenge on Missing Data Imputation (DACMI). In this paper, we present our proposed model: Context-Aware Time Series Imputation (CATSI), a novel framework based on a bidirectional LSTM in which patients’ health states are explicitly captured by learning a “global context vector” from the entire clinical time series. The imputations are then produced with reference to the global context vector. We also incorporate a cross-feature imputation component to explore the complex feature correlations. Empirical evaluations demonstrate that CATSI obtains a normalized root mean square deviation (nRMSD) of 0.1998, which is 10.6% better than that of state-of-the-art models. Further experiments on consecutive missing datasets also illustrate the effectiveness of incorporating the global context in the generation of accurate imputations.

Original languageEnglish
Pages (from-to)411-426
Number of pages16
JournalJournal of Healthcare Informatics Research
Volume4
Issue number4
DOIs
Publication statusPublished - Dec 2020

Scopus Subject Areas

  • Health Informatics
  • Computer Science Applications
  • Information Systems
  • Artificial Intelligence

User-Defined Keywords

  • Clinical time series
  • Electronic health records
  • Missing data imputation

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