ArticlePloS one2014
Predicting progression of Alzheimer's disease using ordinal regression.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.Journal of medical systems · 2019Pooled it
- Incremental validity of the test of practical judgment (TOP-J) in the prediction of diagnosis in preclinical dementia.Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition · 2025Article
- Characterizing heterogeneity in Alzheimer's disease progression: a semiparametric model.Scientific reports · 2025Article
- Early Prediction of Progression to Alzheimer's Disease using Multi-Modality Neuroimages by a Novel Ordinal Learning Model ADPacer.IISE transactions on healthcare systems engineering · 2024Article
- Distributed Support Vector Ordinal Regression over Networks.Entropy (Basel, Switzerland) · 2022Article
- Ordinal classification of the affectation level of 3D-images in Parkinson diseases.Scientific reports · 2021Article
- Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study.Alzheimer's research & therapy · 2019Article
- Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer's disease: a feature selection ensemble combining stability and predictability.BMC medical informatics and decision making · 2018Article
- 18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer's disease (AD) patients at the mild cognitive impairment (MCI) stage.European journal of nuclear medicine and molecular imaging · 2017Article
- Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.BMC medical informatics and decision making · 2017Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- Building better biomarkers: brain models in translational neuroimaging.Nature neuroscience · 2017Review
- The role of machine learning in neuroimaging for drug discovery and development.Psychopharmacology · 2015Review
- The cortical thickness phenotype of individuals with DISC1 translocation resembles schizophrenia.The Journal of clinical investigation · 2015Article
- Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.Journal of Alzheimer's disease : JAD · 2015Article
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Authors and funding
11 authors.
Funding
Abstract
We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = -0.64, ADNI and ρ = -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.
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Registered trials
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