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Machine learning techniques for AD/MCI diagnosis and prognosis

Chapter


Abstract


  • In the past two decades, machine learning techniques have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some of the latest developments in the application of machine learning techniques to AD and MCI diagnosis and prognosis. We will divide our discussion into two parts: single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the various modalities, such as structural T1-weighted imaging, diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.

Authors


  •   Shen, Dinggang (external author)
  •   Wee, Chong-Yaw (external author)
  •   Zhang, Daoqiang (external author)
  •   Zhou, Luping
  •   Yap, Pew-Thian (external author)

Publication Date


  • 2014

Citation


  • Shen, D., Wee, C., Zhang, D., Zhou, L. & Yap, P. (2014). Machine learning techniques for AD/MCI diagnosis and prognosis. In S. Dua, U. Rajendra. Acharya & P. Dua (Eds.), Machine Learning in Healthcare Informatics (pp. 147-179). New York, United States: Springer-Verlag Berlin Heidelberg.

International Standard Book Number (isbn) 13


  • 9783642400162

Scopus Eid


  • 2-s2.0-84927517308

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3058

Book Title


  • Machine Learning in Healthcare Informatics

Has Global Citation Frequency


Start Page


  • 147

End Page


  • 179

Place Of Publication


  • New York, United States

Abstract


  • In the past two decades, machine learning techniques have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some of the latest developments in the application of machine learning techniques to AD and MCI diagnosis and prognosis. We will divide our discussion into two parts: single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the various modalities, such as structural T1-weighted imaging, diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.

Authors


  •   Shen, Dinggang (external author)
  •   Wee, Chong-Yaw (external author)
  •   Zhang, Daoqiang (external author)
  •   Zhou, Luping
  •   Yap, Pew-Thian (external author)

Publication Date


  • 2014

Citation


  • Shen, D., Wee, C., Zhang, D., Zhou, L. & Yap, P. (2014). Machine learning techniques for AD/MCI diagnosis and prognosis. In S. Dua, U. Rajendra. Acharya & P. Dua (Eds.), Machine Learning in Healthcare Informatics (pp. 147-179). New York, United States: Springer-Verlag Berlin Heidelberg.

International Standard Book Number (isbn) 13


  • 9783642400162

Scopus Eid


  • 2-s2.0-84927517308

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3058

Book Title


  • Machine Learning in Healthcare Informatics

Has Global Citation Frequency


Start Page


  • 147

End Page


  • 179

Place Of Publication


  • New York, United States