Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation

Jingwen Xu, Fei Lyu, Pong C. Yuen

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

Abstract

Medical time series have been widely employed for disease prediction. Missing data hinders accurate prediction. While existing imputation methods partially solve the problem, there are two challenges for medical time series: (1) High dimensionality: Existing imputation methods existing methods suffer from the trade-off between accuracy and computational efficiency. (2) Irregularity: Medical time series exhibit the dynamic temporal relationship that changes over varying sampling densities. However, existing methods mainly take the stationary mechanism, which struggles with capturing the dynamic temporal relationships. To overcome the above deficiencies, we propose a Density-Aware Temporal Attentive Step-wise Diffusion Model (DA-TASWDM), which imputes each time step based on a non-iterative diffusion model and captures inter-step dependency with the density-aware time similarity. Specifically, DA-TASWDM exploits two novel modules: (1) Density-Aware Temporal Attention (DA-TA): It correlates inter-step values from the time embedding similarity adjusted with varying sampling densities. (2) Non-Iterative Step-wise Diffusion Imputer (NI-SWDI): It directly recovers the missing values at each time step from noise without diffusion iteration. Compared with the existing methods, DA-TASWDM can achieve promising accuracy without sacrificing computational efficiency. Extensive experimental results on three real-world datasets demonstrate that our method can significantly outperform state-of-the-art methods in both imputation and post-imputation performance.

Original languageEnglish
Title of host publicationCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages2836-2845
Number of pages10
ISBN (Print)9798400701245
DOIs
Publication statusPublished - 21 Oct 2023
Event32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
Duration: 21 Oct 202325 Oct 2023
https://dl.acm.org/doi/proceedings/10.1145/3583780

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/TerritoryUnited Kingdom
CityBirmingham
Period21/10/2325/10/23
Internet address

Scopus Subject Areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

User-Defined Keywords

  • attention
  • diffusion
  • imputation
  • Medical time series

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