These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
311 related articles for article (PubMed ID: 23583359)
1. Modeling disease progression via multi-task learning. Zhou J; Liu J; Narayan VA; Ye J; Neuroimage; 2013 Sep; 78():233-48. PubMed ID: 23583359 [TBL] [Abstract][Full Text] [Related]
2. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Zhang D; Shen D; Neuroimage; 2012 Jan; 59(2):895-907. PubMed ID: 21992749 [TBL] [Abstract][Full Text] [Related]
3. Spatio-temporal Tensor Multi-Task Learning for Predicting Alzheimer's Disease in a Longitudinal study. Zhang Y; Zhou M; Liu T; Lanfranchi V; Yang P Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():979-985. PubMed ID: 36086566 [TBL] [Abstract][Full Text] [Related]
5. The Neuropsychological Correlates of Brain Perfusion and Gray Matter Volume in Alzheimer's Disease. Tai H; Hirano S; Sakurai T; Nakano Y; Ishikawa A; Kojima K; Li H; Shimada H; Kashiwado K; Mukai H; Horikoshi T; Sugiyama A; Uno T; Kuwabara S J Alzheimers Dis; 2020; 78(4):1639-1652. PubMed ID: 33185599 [TBL] [Abstract][Full Text] [Related]
6. Longitudinal Exposure-Response Modeling of Multiple Indicators of Alzheimer's Disease Progression. Polhamus DG; Dolton MJ; Rogers JA; Honigberg L; Jin JY; Quartino A J Prev Alzheimers Dis; 2023; 10(2):212-222. PubMed ID: 36946448 [TBL] [Abstract][Full Text] [Related]
7. Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer's Disease Dynamic Prediction. Zhang Y; Liu T; Lanfranchi V; Yang P IEEE J Transl Eng Health Med; 2023; 11():1-12. PubMed ID: 36478772 [TBL] [Abstract][Full Text] [Related]
8. Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis. Zhu X; Suk HI; Lee SW; Shen D Brain Imaging Behav; 2016 Sep; 10(3):818-28. PubMed ID: 26254746 [TBL] [Abstract][Full Text] [Related]
9. Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease. Cao P; Liu X; Liu H; Yang J; Zhao D; Huang M; Zaiane O Comput Methods Programs Biomed; 2018 Aug; 162():19-45. PubMed ID: 29903486 [TBL] [Abstract][Full Text] [Related]
10. Predicting clinical scores from magnetic resonance scans in Alzheimer's disease. Stonnington CM; Chu C; Klöppel S; Jack CR; Ashburner J; Frackowiak RS; Neuroimage; 2010 Jul; 51(4):1405-13. PubMed ID: 20347044 [TBL] [Abstract][Full Text] [Related]
11. Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network. Liang W; Zhang K; Cao P; Liu X; Yang J; Zaiane O Comput Biol Med; 2021 Nov; 138():104935. PubMed ID: 34656869 [TBL] [Abstract][Full Text] [Related]
12. Derivation of a new ADAS-cog composite using tree-based multivariate analysis: prediction of conversion from mild cognitive impairment to Alzheimer disease. Llano DA; Laforet G; Devanarayan V; Alzheimer Dis Assoc Disord; 2011; 25(1):73-84. PubMed ID: 20847637 [TBL] [Abstract][Full Text] [Related]
13. Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images. Dong Q; Zhang J; Li Q; Wang J; Leporé N; Thompson PM; Caselli RJ; Ye J; Wang Y; J Alzheimers Dis; 2020; 75(3):971-992. PubMed ID: 32390615 [TBL] [Abstract][Full Text] [Related]
14. Detecting treatment effects with combinations of the ADAS-cog items in patients with mild and moderate Alzheimer's disease. Ihl R; Ferris S; Robert P; Winblad B; Gauthier S; Tennigkeit F Int J Geriatr Psychiatry; 2012 Jan; 27(1):15-21. PubMed ID: 21384431 [TBL] [Abstract][Full Text] [Related]
15. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. Bhagwat N; Viviano JD; Voineskos AN; Chakravarty MM; PLoS Comput Biol; 2018 Sep; 14(9):e1006376. PubMed ID: 30216352 [TBL] [Abstract][Full Text] [Related]
16. The validity and reliability of the Turkish version of Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) in patients with mild and moderate Alzheimer's disease and normal subjects. Mavioglu H; Gedizlioglu M; Akyel S; Aslaner T; Eser E Int J Geriatr Psychiatry; 2006 Mar; 21(3):259-65. PubMed ID: 16477580 [TBL] [Abstract][Full Text] [Related]
17. Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry. Tsao S; Gajawelli N; Zhou J; Shi J; Ye J; Wang Y; Leporé N Brain Behav; 2017 Jul; 7(7):e00733. PubMed ID: 28729939 [TBL] [Abstract][Full Text] [Related]
18. A study of the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) in an Icelandic elderly population. Hannesdóttir K; Snaedal J Nord J Psychiatry; 2002; 56(3):201-6. PubMed ID: 12079572 [TBL] [Abstract][Full Text] [Related]
19. Clinical trials in Alzheimer's disease. Calculating Alzheimer's Disease Assessment Scale-cognitive subsection with the data from the consortium to establish a registry for Alzheimer's disease. Gillen TE; Gregg KM; Yuan H; Kurth MC; Krishnan KR Psychopharmacol Bull; 2001; 35(2):83-96. PubMed ID: 12397889 [TBL] [Abstract][Full Text] [Related]
20. An MRI brain atrophy and lesion index to assess the progression of structural changes in Alzheimer's disease, mild cognitive impairment, and normal aging: a follow-up study. Zhang N; Song X; Zhang Y; Chen W; D'Arcy RC; Darvesh S; Fisk JD; Rockwood K; J Alzheimers Dis; 2011; 26 Suppl 3():359-67. PubMed ID: 21971475 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]