A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
Authors
Keywords
-
Journal
Scientific Reports
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-01-29
DOI
10.1038/s41598-021-82098-3
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Re-epithelialization and immune cell behaviour in an ex vivo human skin model
- (2020) Ana Rakita et al. Scientific Reports
- Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data
- (2020) Shaker El-Sappagh et al. NEUROCOMPUTING
- Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data
- (2020) Shaker El-Sappagh et al. Future Generation Computer Systems-The International Journal of eScience
- A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease
- (2019) Simeon Spasov et al. NEUROIMAGE
- Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles
- (2019) Aldenor G. Santos et al. Scientific Reports
- An interpretable machine learning model for diagnosis of Alzheimer's disease
- (2019) Diptesh Das et al. PeerJ
- A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual
- (2019) Magda Bucholc et al. EXPERT SYSTEMS WITH APPLICATIONS
- Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
- (2019) Charles K. Fisher et al. Scientific Reports
- A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data
- (2018) Kan Li et al. Alzheimers & Dementia
- Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging
- (2018) Hongyoon Choi et al. BEHAVIOURAL BRAIN RESEARCH
- Ensemble of random forests One vs . Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares
- (2018) J. Ramírez et al. JOURNAL OF NEUROSCIENCE METHODS
- Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis
- (2018) Manhua Liu et al. NEUROINFORMATICS
- Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images
- (2018) Donghuan Lu et al. Scientific Reports
- Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks
- (2018) Tingyan Wang et al. Scientific Reports
- An ontology-based interpretable fuzzy decision support system for diabetes diagnosis
- (2018) Shaker El-Sappagh et al. IEEE Access
- iForest: Interpreting Random Forests via Visual Analytics
- (2018) Xun Zhao et al. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
- A Disease State Fingerprint for Evaluation of Alzheimer's Disease
- (2018) Jussi Mattila et al. JOURNAL OF ALZHEIMERS DISEASE
- In situ immune response and mechanisms of cell damage in central nervous system of fatal cases microcephaly by Zika virus
- (2018) Raimunda S. S. Azevedo et al. Scientific Reports
- Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)
- (2018) Amina Adadi et al. IEEE Access
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- Multi-task deep convolutional neural network for cancer diagnosis
- (2018) Qing Liao et al. NEUROCOMPUTING
- A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease
- (2017) Tong Tong et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease
- (2017) Liqiang Nie et al. IEEE Transactions on Neural Networks and Learning Systems
- Alzheimer's Disease and the Amyloid-β Peptide
- (2017) M. Paul Murphy et al. JOURNAL OF ALZHEIMERS DISEASE
- Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
- (2017) Michiru Nishita et al. Scientific Reports
- On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey
- (2016) Ane Alberdi et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- The Alzheimer's Disease Neuroimaging Initiative 2 PET Core: 2015
- (2015) William J. Jagust et al. Alzheimers & Dementia
- Domain Transfer Learning for MCI Conversion Prediction
- (2015) Bo Cheng et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- The assessment of dementia in primary care
- (2015) E. Dodd et al. Journal of Psychiatric and Mental Health Nursing
- Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
- (2015) Elaheh Moradi et al. NEUROIMAGE
- Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
- (2014) Sebastián Maldonado et al. INFORMATION SCIENCES
- Amyloid-Beta: A Crucial Factor in Alzheimer's Disease
- (2014) Saeed Sadigh-Eteghad et al. MEDICAL PRINCIPLES AND PRACTICE
- Revision of the Cognitive Assessment for Dementia, iPad Version (CADi2)
- (2014) Keiichi Onoda et al. PLoS One
- Cross-validation pitfalls when selecting and assessing regression and classification models
- (2014) Damjan Krstajic et al. Journal of Cheminformatics
- Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness
- (2014) A.V. Lebedev et al. NeuroImage-Clinical
- Explaining prediction models and individual predictions with feature contributions
- (2013) Erik Štrumbelj et al. KNOWLEDGE AND INFORMATION SYSTEMS
- Modeling disease progression via multi-task learning
- (2013) Jiayu Zhou et al. NEUROIMAGE
- Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
- (2012) Chong-Yaw Wee et al. HUMAN BRAIN MAPPING
- Random forest-based similarity measures for multi-modal classification of Alzheimer's disease
- (2012) Katherine R. Gray et al. NEUROIMAGE
- Characterizing Alzheimer's disease using a hypometabolic convergence index
- (2011) Kewei Chen et al. NEUROIMAGE
- Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
- (2011) Daoqiang Zhang et al. NEUROIMAGE
- Classification of Alzheimer Disease, Mild Cognitive Impairment, and Normal Cognitive Status with Large-Scale Network Analysis Based on Resting-State Functional MR Imaging
- (2011) Gang Chen et al. RADIOLOGY
- An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
- (2010) Johan Huysmans et al. DECISION SUPPORT SYSTEMS
- Use of Wrapper Algorithms Coupled with a Random Forests Classifier for Variable Selection in Large-Scale Genomic Association Studies
- (2010) Andrei S. Rodin et al. JOURNAL OF COMPUTATIONAL BIOLOGY
- Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population
- (2010) Chris Hinrichs et al. NEUROIMAGE
- A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
- (2009) Bjoern H Menze et al. BMC BIOINFORMATICS
- Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI
- (2009) Susan M. Landau et al. NEUROBIOLOGY OF AGING
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started