Development and validation of a model to predict the progression of Alzheimer's disease.
Background Cognition monitoring is crucial for care planning in people with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Objective To develop a machine learning model to assist cognition monitoring. Design Florey Fusion Model (FFM) was constructed and validated in two phases: (i) m...
| Publicado en: | Age & Ageing Vol. 54; no. 7; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187169239&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187169239 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Jul2025 vid: 54 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 187169239 10.1093/ageing/afaf198 ppf: 1 ppct: 10 formats: tig: atl: Development and validation of a model to predict the progression of Alzheimer's disease. aug: au: Chu, Chenyin Wang, Yihan Huynh, Andrew L H Ng, Ka Weng Liu, Shu Ji, Guangyan Doecke, James Fripp, Jurgen Masters, Colin L Goudey, Benjamin Jin, Liang Pan, Yijun affil: Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia Department of Medicine, Austin Health, Heidelberg, Victoria, Australia Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia Florey Institutes of Neuroscience and Mental Health, Melbourne, Victoria, Australia Australian E-Health Research Centre, CSIRO, Herston, Queensland, Australia Australian Biocommons, The University of Melbourne, Carlton, Victoria, Australia su: Australia Risk assessment Random forest algorithms Alzheimer's disease Prediction models Mild cognitive impairment Cognitive testing Research funding Receiver operating characteristic curves Questionnaires Descriptive statistics Support vector machines Simulation methods in education Machine learning Comparative studies Disease progression sug: subj: Australia Risk assessment Random forest algorithms Alzheimer's disease Prediction models Mild cognitive impairment Cognitive testing Research funding Receiver operating characteristic curves Questionnaires Descriptive statistics Support vector machines Simulation methods in education Machine learning Comparative studies Disease progression keyword: cognition monitoring machine learning mild cognitive impairment older people cognition monitoring machine learning mild cognitive impairment older people ab: Background Cognition monitoring is crucial for care planning in people with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Objective To develop a machine learning model to assist cognition monitoring. Design Florey 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. Methods This 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. Results The 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. Conclusion The 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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