205 related articles for article (PubMed ID: 35457705)
1. A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.
Lin RH; Wang CC; Tung CW
Int J Environ Res Public Health; 2022 Apr; 19(8):. PubMed ID: 35457705
[TBL] [Abstract][Full Text] [Related]
2. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
Moradi E; Pepe A; Gaser C; Huttunen H; Tohka J;
Neuroimage; 2015 Jan; 104():398-412. PubMed ID: 25312773
[TBL] [Abstract][Full Text] [Related]
3. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.
Spasov S; Passamonti L; Duggento A; Liò P; Toschi N;
Neuroimage; 2019 Apr; 189():276-287. PubMed ID: 30654174
[TBL] [Abstract][Full Text] [Related]
4. ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease.
Apostolova LG; Hwang KS; Kohannim O; Avila D; Elashoff D; Jack CR; Shaw L; Trojanowski JQ; Weiner MW; Thompson PM;
Neuroimage Clin; 2014; 4():461-72. PubMed ID: 24634832
[TBL] [Abstract][Full Text] [Related]
5. c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.
Jemimah S; AlShehhi A;
BMC Med Genomics; 2023 Oct; 16(Suppl 2):244. PubMed ID: 37833700
[TBL] [Abstract][Full Text] [Related]
6. An Efficient Combination among sMRI, CSF, Cognitive Score, and
Khatri U; Kwon GR
Comput Intell Neurosci; 2020; 2020():8015156. PubMed ID: 32565773
[TBL] [Abstract][Full Text] [Related]
7. A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.
Massetti N; Russo M; Franciotti R; Nardini D; Mandolini GM; Granzotto A; Bomba M; Delli Pizzi S; Mosca A; Scherer R; Onofrj M; Sensi SL; ;
J Alzheimers Dis; 2022; 85(4):1639-1655. PubMed ID: 34958014
[TBL] [Abstract][Full Text] [Related]
8. Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.
Lin Y; Huang K; Xu H; Qiao Z; Cai S; Wang Y; Huang L;
Clin Neurophysiol; 2020 Oct; 131(10):2429-2439. PubMed ID: 32829290
[TBL] [Abstract][Full Text] [Related]
9. Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.
Dimitriadis SI; Liparas D; Tsolaki MN;
J Neurosci Methods; 2018 May; 302():14-23. PubMed ID: 29269320
[TBL] [Abstract][Full Text] [Related]
10. Biomarker Extraction Based on Subspace Learning for the Prediction of Mild Cognitive Impairment Conversion.
Li Y; Fang Y; Wang J; Zhang H; Hu B
Biomed Res Int; 2021; 2021():5531940. PubMed ID: 34513992
[TBL] [Abstract][Full Text] [Related]
11. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.
Yi F; Yang H; Chen D; Qin Y; Han H; Cui J; Bai W; Ma Y; Zhang R; Yu H
BMC Med Inform Decis Mak; 2023 Jul; 23(1):137. PubMed ID: 37491248
[TBL] [Abstract][Full Text] [Related]
12. Augmenting Imaging Biomarker Performance with Blood-Based Gene Expression Levels for Predicting Alzheimer's Disease Progression.
Dobromyslin VI; Megherbi DB;
J Alzheimers Dis; 2022; 87(2):583-594. PubMed ID: 35311706
[TBL] [Abstract][Full Text] [Related]
13. Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.
Wang Y; Gao R; Wei T; Johnston L; Yuan X; Zhang Y; Yu Z;
J Transl Med; 2024 Mar; 22(1):265. PubMed ID: 38468358
[TBL] [Abstract][Full Text] [Related]
14. Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease.
Chincarini A; Bosco P; Calvini P; Gemme G; Esposito M; Olivieri C; Rei L; Squarcia S; Rodriguez G; Bellotti R; Cerello P; De Mitri I; Retico A; Nobili F;
Neuroimage; 2011 Sep; 58(2):469-80. PubMed ID: 21718788
[TBL] [Abstract][Full Text] [Related]
15. Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.
Ramírez J; Górriz JM; Ortiz A; Martínez-Murcia FJ; Segovia F; Salas-Gonzalez D; Castillo-Barnes D; Illán IA; Puntonet CG;
J Neurosci Methods; 2018 May; 302():47-57. PubMed ID: 29242123
[TBL] [Abstract][Full Text] [Related]
16. Comparing different algorithms for the course of Alzheimer's disease using machine learning.
Tang X; Liu J
Ann Palliat Med; 2021 Sep; 10(9):9715-9724. PubMed ID: 34628897
[TBL] [Abstract][Full Text] [Related]
17. Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features.
Wang W; Peng J; Hou J; Yuan Z; Xie W; Mao G; Pan Y; Shao Y; Shu Z
Aging Clin Exp Res; 2023 Aug; 35(8):1721-1730. PubMed ID: 37405620
[TBL] [Abstract][Full Text] [Related]
18. Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches.
Chang CH; Lin CH; Liu CY; Huang CS; Chen SJ; Lin WC; Yang HT; Lane HY
J Psychopharmacol; 2021 Mar; 35(3):265-272. PubMed ID: 33586518
[TBL] [Abstract][Full Text] [Related]
19. Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia.
Frölich L; Peters O; Lewczuk P; Gruber O; Teipel SJ; Gertz HJ; Jahn H; Jessen F; Kurz A; Luckhaus C; Hüll M; Pantel J; Reischies FM; Schröder J; Wagner M; Rienhoff O; Wolf S; Bauer C; Schuchhardt J; Heuser I; Rüther E; Henn F; Maier W; Wiltfang J; Kornhuber J
Alzheimers Res Ther; 2017 Oct; 9(1):84. PubMed ID: 29017593
[TBL] [Abstract][Full Text] [Related]
20. A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment.
Grassi M; Perna G; Caldirola D; Schruers K; Duara R; Loewenstein DA
J Alzheimers Dis; 2018; 61(4):1555-1573. PubMed ID: 29355115
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
