ArticleMedical image analysis2018
Conversion and time-to-conversion predictions of mild cognitive impairment using low-rank affinity pursuit denoising and matrix completion.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning.BMC medical informatics and decision making · 2025Article
- Brain Age Estimation on a Dementia Cohort Using FLAIR MRI Biomarkers.AJNR. American journal of neuroradiology · 2023Article
- Mapping cerebral atrophic trajectory from amnestic mild cognitive impairment to Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Detecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers.NeuroImage. Clinical · 2023Article
- SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring.Frontiers in physiology · 2023Article
- Lossless Medical Image Compression by Using Difference Transform.Entropy (Basel, Switzerland) · 2022Article
- The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification.Frontiers in neuroscience · 2022Article
- Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?Diagnostics (Basel, Switzerland) · 2021Review
- Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.IEEE journal of biomedical and health informatics · 2021Article
- Low-Rank Matrix Denoising Algorithm-Based MRI Image Feature for Therapeutic Effect Evaluation of NCRT on Rectal Cancer.Journal of healthcare engineering · 2021Article
- Predicting dysfunctional age-related task activations from resting-state network alterations.NeuroImage · 2020Article
- Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data.Medical image analysis · 2020Article
- Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease.NeuroImage. Clinical · 2020Article
- Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record-based approach.Alzheimer's & dementia (New York, N. Y.) · 2020Article
- Latent Representation Learning for Alzheimer's Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data.IEEE transactions on medical imaging · 2019Article
- Early temporal characteristics of elderly patient cognitive impairment in electronic health records.BMC medical informatics and decision making · 2019Article
- EARLY PREDICTION OF ALZHEIMER'S DISEASE DEMENTIA BASED ON BASELINE HIPPOCAMPAL MRI AND 1-YEAR FOLLOW-UP COGNITIVE MEASURES USING DEEP RECURRENT NEURAL NETWORKS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.Human brain mapping · 2019Article
- Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.NeuroImage. Clinical · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In this paper, we aim to predict conversion and time-to-conversion of mild cognitive impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross-sectional and longitudinal studies. However, such data are often heterogeneous, high-dimensional, noisy, and incomplete. We thus propose a framework that includes sparse feature selection, low-rank affinity pursuit denoising (LRAD), and low-rank matrix completion (LRMC) in this study. Specifically, we first use sparse linear regressions to remove unrelated features. Then, considering the heterogeneity of the MCI data, which can be assumed as a union of multiple subspaces, we propose to use a low rank subspace method (i.e., LRAD) to denoise the data. Finally, we employ LRMC algorithm with three data fitting terms and one inequality constraint for joint conversion and time-to-conversion predictions. Our framework aims to answer a very important but yet rarely explored question in AD study, i.e., when will the MCI convert to AD? This is different from survival analysis, which provides the probabilities of conversion at different time points that are mainly used for global analysis, while our time-to-conversion prediction is for each individual subject. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC and other state-of-the-art methods. Our method achieves a maximal pMCI classification accuracy of 84% and time prediction correlation of 0.665.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.