Qian, L;
Ibrahim, Z;
Zhang, A;
Dobson, RJB;
(2023)
Addressing Class Imbalance in Electronic Health Records Data Imputation.
In:
Proceedings of the 6th International Workshop on Knowledge Discovery From Healthcare Data.
CEUR Workshop Proceedings
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Abstract
Imputing missing values in imbalanced datasets remains an open challenge. Most methods assume data are missing at random or follow a standard distribution, lacking robustness for complex real-world data. Electronic health records exhibit severe class imbalance with non-random missingness, hindering model performance. We propose M3-BRITS for greater scalability and flexibility, modeling temporal and cross-feature correlations to impute missing data, by optimizing sample similarity with deep metric learning for self-supervised learning. Evaluating imputation alone avoids reduced diversity and model bias from joint downstream tasks. Our model achieves superior performance to all baseline methods on four real-world datasets. This shows promise for increasing model scalability and flexibility to handle complex real-world data.
| Type: | Proceedings paper |
|---|---|
| Title: | Addressing Class Imbalance in Electronic Health Records Data Imputation |
| Event: | 6th International Workshop on Knowledge Discovery From Healthcare Data |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ceur-ws.org/Vol-3479/paper7.pdf |
| Language: | English |
| Additional information: | © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) |
| Keywords: | Missingness, Imbalance, Imputation, Self-supervised learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10179616 |
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