Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/8953 |
| _version_ | 1797587059600261120 |
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| author | Zeyuan Zhao Ping Li Yongjie Dai Zhaoe Min Lei Chen |
| author_facet | Zeyuan Zhao Ping Li Yongjie Dai Zhaoe Min Lei Chen |
| author_sort | Zeyuan Zhao |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications. |
| first_indexed | 2024-03-11T00:31:53Z |
| format | Article |
| id | doaj.art-fe55dcffa0214d3783ba740ef8c59805 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:31:53Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fe55dcffa0214d3783ba740ef8c598052023-11-18T22:39:44ZengMDPI AGApplied Sciences2076-34172023-08-011315895310.3390/app13158953Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression PredictionZeyuan Zhao0Ping Li1Yongjie Dai2Zhaoe Min3Lei Chen4Bell Honor School, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaAlzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications.https://www.mdpi.com/2076-3417/13/15/8953Alzheimer’s diseasedisease progression predictiondeep sequence learningtrustworthy evidential learningadaptive weighting scheme |
| spellingShingle | Zeyuan Zhao Ping Li Yongjie Dai Zhaoe Min Lei Chen Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction Applied Sciences Alzheimer’s disease disease progression prediction deep sequence learning trustworthy evidential learning adaptive weighting scheme |
| title | Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction |
| title_full | Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction |
| title_fullStr | Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction |
| title_full_unstemmed | Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction |
| title_short | Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction |
| title_sort | multi task deep evidential sequence learning for trustworthy alzheimer s disease progression prediction |
| topic | Alzheimer’s disease disease progression prediction deep sequence learning trustworthy evidential learning adaptive weighting scheme |
| url | https://www.mdpi.com/2076-3417/13/15/8953 |
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