EAPR : explainable and augmented patient representation learning for disease prediction
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law..
Patient representation learning aims to encode meaningful information about the patient's Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly improve the performance of disease prediction models. However, the inherent shortcomings of deep learning models, such as the need for massive amounts of labeled data and inexplicability, limit the performance of deep learning-based Patient representation learning methods to further improvements. In particular, learning robust patient representations is challenging when patient data is missing or insufficient. Although data augmentation techniques can tackle this deficiency, the complex data processing further weakens the inexplicability of patient representation learning models. To address the above challenges, this paper proposes an Explainable and Augmented Patient Representation Learning for disease prediction (EAPR). EAPR utilizes data augmentation controlled by confidence interval to enhance patient representation in the presence of limited patient data. Moreover, EAPR proposes to use two-stage gradient backpropagation to address the problem of unexplainable patient representation learning models due to the complex data enhancement process. The experimental results on real clinical data validate the effectiveness and explainability of the proposed approach.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Health information science and systems - 11(2023), 1 vom: 16. Dez., Seite 53 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Jiancheng [VerfasserIn] |
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| Links: |
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| Themen: |
Data augmentation |
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| Anmerkungen: |
Date Revised 17.12.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1007/s13755-023-00256-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Patient representation learning aims to encode meaningful information about the patient's Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly improve the performance of disease prediction models. However, the inherent shortcomings of deep learning models, such as the need for massive amounts of labeled data and inexplicability, limit the performance of deep learning-based Patient representation learning methods to further improvements. In particular, learning robust patient representations is challenging when patient data is missing or insufficient. Although data augmentation techniques can tackle this deficiency, the complex data processing further weakens the inexplicability of patient representation learning models. To address the above challenges, this paper proposes an Explainable and Augmented Patient Representation Learning for disease prediction (EAPR). EAPR utilizes data augmentation controlled by confidence interval to enhance patient representation in the presence of limited patient data. Moreover, EAPR proposes to use two-stage gradient backpropagation to address the problem of unexplainable patient representation learning models due to the complex data enhancement process. The experimental results on real clinical data validate the effectiveness and explainability of the proposed approach | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Data augmentation | |
| 650 | 4 | |a Disease prediction | |
| 650 | 4 | |a Explanation method | |
| 650 | 4 | |a Patient representation | |
| 700 | 1 | |a Xu, Yonghui |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Bicui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yibowen |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Xiaofang |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Lizhen |e verfasserin |4 aut | |
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