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. Author manuscript; available in PMC: 2018 Oct 1.
Published in final edited form as: Int J Med Inform. 2017 Jul 24;106:48–56. doi: 10.1016/j.ijmedinf.2017.07.002

Table 7.

Datasets, data analysis methods, and key findings for selected studies in research foci 5 - predicting AD progression.

Study Datasets Primary data analysis methods Key findings
Parikh et al. (58) Neuropsychological, clinical and psychiatric measures for 96 patients with mild AD (45 faster and 51 slower progresses) enrolled at AD Center at the University of Texas Southwestern Medical Center during 1995–2011 Stepwise logistic regression Several neuropsychological performance features can predict cognitive decline rate in mild AD.
Stonnington et al. (59) MRI scans in ADNI (113 AD, 351 MCI, 122 HC) and MMSE, dementia rating scale (DRS), Auditory Verbal Learning Test (AVLT) measures from Mayo clinic (73 AD and 91 HC) Relevance vector regression (RVR) RVR can predict MMSE, Dementia Rating Scale (DRS) and Alzheimer’s Disease Assessment Scale—Cognitive subtest ADAS and can aid in AD diagnosis and clinical outcome prediction.

Note: ADNI, Alzheimer’s Disease Neuroimaging Initiative database; MCI, mild-cognitive impairment; SMCI, stable mild cognitive impairment; PMCI, progressive mild cognitive impairment; HC, healthy control.