research-article

Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis

Authors:

Jiangtao WangAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3558 - 3568

https://doi.org/10.1145/3442381.3449855

Published: 03 June 2021 Publication History

Abstract

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from life-threatening systemic problems and need to be carefully monitored in ICUs. An intelligent prognosis can help physicians take an early intervention, prevent adverse outcomes, and optimize the medical resource allocation, which is urgently needed, especially in this ongoing global pandemic crisis. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, the rarity of the cases, and privacy concerns. In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data. The transferred parameters are further trained to imitate the teacher model’s representation based on distillation, which embeds the health status more comprehensively on the source dataset. We conduct Length-of-Stay prediction experiments for patients in ICUs on real-world COVID-19 datasets. The experiment results indicate that our proposed model consistently outperforms competitive baseline methods. In order to further verify the scalability of o deal with different clinical tasks on different EMR datasets, we conduct an additional mortality prediction experiment on End-Stage Renal Disease datasets. The extensive experiments demonstrate that an benefit the prognosis for emerging pandemics and other diseases with limited EMR.

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Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis
1. Applied computing
  1. Life and medical sciences

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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

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Jure Leskovec
Stanford
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Jožef Stefan Institute
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Google
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Tsinghua University
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Published: 03 June 2021

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https://doi.org/10.1109/TSE.2023.3348515
Li PParvej MZhang CGuo SZhang J(2023)Advances in the Development of Representation Learning and Its Innovations against COVID-19COVID10.3390/covid30900963:9(1389-1415)Online publication date: 13-Sep-2023
https://doi.org/10.3390/covid3090096
Xu YYang KZhang CZou PWang ZDing HZhao JWang YXie BElkind E(2023)VecoCareProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/547(4921-4929)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/547
Chen LWang JThakuriah P(2023)Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning ApproachACM Transactions on Computing for Healthcare10.1145/36171794:4(1-19)Online publication date: 13-Oct-2023
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Huang CWang LHan X(2023)Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration NetworksProceedings of the ACM Web Conference 202310.1145/3543507.3583874(4188-4199)Online publication date: 30-Apr-2023
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Xu YChu XYang KWang ZZou PDing HZhao JWang YXie B(2023)SeqCare: Sequential Training with External Medical Knowledge Graph for Diagnosis Prediction in Healthcare DataProceedings of the ACM Web Conference 202310.1145/3543507.3583543(2819-2830)Online publication date: 30-Apr-2023
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Ding RZhou YXu JXie YLiang QRen HWang YChen YWang LHuang M(2023)Cross-Hospital Sepsis Early Detection via Semi-Supervised Optimal Transport With Self-Paced EnsembleIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.325320827:6(3049-3060)Online publication date: Jun-2023
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