A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods

Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetr...

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Vydáno v:	Mobile networks and applications Ročník 26; číslo 6; s. 2341 - 2352
Hlavní autoři:	Pang, Zhen, Wang, Xiang, Wang, Xulong, Qi, Jun, Zhao, Zhong, Gao, Yuan, Yang, Yun, Yang, Po
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2021 Springer Nature B.V
Témata:	Algorithms Alzheimer's disease Biomarkers Cerebrospinal fluid Cognitive tasks Communications Engineering Computer Communication Networks Data mining Diagnosis Disease Electrical Engineering Engineering Health care facilities IT in Business Machine learning Magnetic resonance imaging Medical imaging Modal data Networks Neurological diseases Physicians Positron emission Tomography Multi-modal data Technical architecture Auxiliary diagnosis Disease progression prediction Multi-task learning Classification
ISSN:	1383-469X, 1572-8153
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Abstract	Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer’s Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient’s cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis.
AbstractList	Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer’s Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient’s cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis. Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer’s Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient’s cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis.
Author	Gao, Yuan Wang, Xiang Pang, Zhen Yang, Yun Zhao, Zhong Qi, Jun Wang, Xulong Yang, Po
Author_xml	– sequence: 1 givenname: Zhen surname: Pang fullname: Pang, Zhen organization: National Pilot School of Software for Yunnan University – sequence: 2 givenname: Xiang surname: Wang fullname: Wang, Xiang organization: National Pilot School of Software for Yunnan University – sequence: 3 givenname: Xulong surname: Wang fullname: Wang, Xulong organization: National Pilot School of Software for Yunnan University – sequence: 4 givenname: Jun orcidid: 0000-0002-8761-8318 surname: Qi fullname: Qi, Jun email: jun.qi@xjtlu.edu.cn organization: Department of Computer Science and Software Engineering, Xi’an JiaoTong-Liverpool University – sequence: 5 givenname: Zhong surname: Zhao fullname: Zhao, Zhong email: wasx-1128new@163.com organization: Department of Neurology, the First People’s Hospital of Yunnan Province – sequence: 6 givenname: Yuan surname: Gao fullname: Gao, Yuan organization: Department of Neurology, the First People’s Hospital of Yunnan Province – sequence: 7 givenname: Yun surname: Yang fullname: Yang, Yun email: yang@ynu.edu.cn organization: National Pilot School of Software for Yunnan University – sequence: 8 givenname: Po surname: Yang fullname: Yang, Po organization: Department of Computer Science Faculty of Engineering, University of Sheffield
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Snippet	Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is...
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SubjectTerms	Algorithms Alzheimer's disease Biomarkers Cerebrospinal fluid Cognitive tasks Communications Engineering Computer Communication Networks Data mining Diagnosis Disease Electrical Engineering Engineering Health care facilities IT in Business Machine learning Magnetic resonance imaging Medical imaging Modal data Networks Neurological diseases Physicians Positron emission Tomography
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Title	A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods
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