Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction
Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheim...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020025 |
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| author | Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan |
| author_facet | Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan |
| author_sort | Jin Liu |
| collection | DOAJ |
| description | Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner. |
| format | Article |
| id | doaj-art-2ffa80bb3a8243c49ce8ea7b9e67c65d |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-2ffa80bb3a8243c49ce8ea7b9e67c65d2025-02-03T10:19:58ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017382884210.26599/BDMA.2024.9020025Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score PredictionJin Liu0Xu Tian1Hanhe Lin2Hong-Dong Li3Yi Pan4Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UKHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaFaculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaAccurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner.https://www.sciopen.com/article/10.26599/BDMA.2024.9020025multi-task learningalzheimer’s disease diagnosismini-mental state examination score prediction |
| spellingShingle | Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction Big Data Mining and Analytics multi-task learning alzheimer’s disease diagnosis mini-mental state examination score prediction |
| title | Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction |
| title_full | Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction |
| title_fullStr | Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction |
| title_full_unstemmed | Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction |
| title_short | Multi-Task Learning for Alzheimer’s Disease Diagnosis and Mini-Mental State Examination Score Prediction |
| title_sort | multi task learning for alzheimer s disease diagnosis and mini mental state examination score prediction |
| topic | multi-task learning alzheimer’s disease diagnosis mini-mental state examination score prediction |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020025 |
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