Abstract
Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.
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Acknowledgments
Some of the proposed methods were developed during the author’s appointment at Technische Universität München (TUM), Munich, Germany. The author thanks Prof. Dr. Stefan Kramer for his academic guidance during the PhD study at Technische Universität München and Johannes Gutenberg-Universität Mainz.
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Li, R. (2018). Data Mining and Machine Learning Methods for Dementia Research. In: Perneczky, R. (eds) Biomarkers for Alzheimer’s Disease Drug Development. Methods in Molecular Biology, vol 1750. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7704-8_25
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DOI: https://doi.org/10.1007/978-1-4939-7704-8_25
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