Abstract
Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received considerable attention. Such predictions will provide early warning of a patient’s health condition to healthcare professionals so that timely interventions can be taken. This prediction task is challenging since EHR data are intrinsically irregular, with not only many missing values but also varying time intervals between medical records. Existing approaches focus on exploiting the variable correlations in patient medical records to impute missing values and establishing time-decay mechanisms to deal with such irregularity. This paper presents a novel contrastive learning-based imputation-prediction network for predicting in-hospital mortality risks using EHR data. Our approach introduces graph analysis-based patient stratification modeling in the imputation process to group similar patients. This allows information of similar patients only to be used, in addition to personal contextual information, for missing value imputation. Moreover, our approach can integrate contrastive learning into the proposed network architecture to enhance patient representation learning and predictive performance on the classification task. Experiments on two real-world EHR datasets show that our approach outperforms the state-of-the-art approaches in both imputation and prediction tasks.
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Notes
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The implementation code is available at https://github.com/liulab1356/CL-ImpPreNet.
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Acknowledgement
This research is partially funded by the ARC Centre of Excellence for Automated Decision-Making and Society (CE200100005) by the Australian Government through the Australian Research Council.
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The experimental datasets used for this work are obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset and the eICU Collaborative Research dataset. These data were used under license. The authors declare that they have no conflicts of interest. This article does not contain any studies involving human participants performed by any of the authors.
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Liu, Y., Zhang, Z., Qin, S., Salim, F.D., Yepes, A.J. (2023). Contrastive Learning-Based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling Using EHRs. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14174. Springer, Cham. https://doi.org/10.1007/978-3-031-43427-3_26
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