ArticleAge and ageing2025
Development and validation of a model to predict the progression of Alzheimer's disease.
Article in Age and ageing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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12 authors.
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Abstract
backgroundCognition monitoring is crucial for care planning in people with mild cognitive impairment (MCI) and Alzheimer's dementia (AD).
objectiveTo develop a machine learning model to assist cognition monitoring.
designFlorey Fusion Model (FFM) was constructed and validated in two phases: (i) model development and cross-validation using data collected via the Australian Imaging, Biomarker, and Lifestyle of Ageing (AIBL) study, and (ii) simulation and missing data trials with 30 new participants.
methodsThis prognostic study recruited 238 participants in the AIBL study. Support vector machine, gradient boosting and random forest were trialled to develop the FFM. Cognitive decline was assessed via changes in Clinical Dementia Rating Sum of Boxes (CDR-SB) and Mini-Mental State Examination (MMSE) scores. Model performance was evaluated by cross validation and compared against baseline models.
resultsThe FFM achieved a median area under receive character curve (AUC-ROC) of 0.91 (IQR 0.87-0.93) for MCI-to-AD progression prediction. A mean absolute error (MAE) of 1.32 (IQR 1.30-1.33) for CDR-SB and 1.51 (IQR 1.50-1.52) for MMSE was achieved for 3-year cognition forecast. Simulation and missing data trials yielded up to 94% accuracy for MCI-to-AD conversion and MAEs of 1.27-2.12 for CDR-SB score prediction.
conclusionThe FFM holds the potential to facilitate cognition monitoring in people with MCI/AD; however, a larger trial will be required to refine it as a clinical grade tool.
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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 Socratic graph.