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Addressing Class Imbalance in Electronic Health Records Data Imputation

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 Green open access

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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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