Age and ageing · 2025
Development and validation of a model to predict the progression of Alzheimer's disease.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chu Chenyin, Wang Yihan, L H Huynh Andrew, Ng Ka Weng, Liu Shu, Ji Guangyan, Doecke James, Fripp Jurgen, Masters Colin L, Goudey Benjamin, Jin Liang, Pan Yijun
Аннотация
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.
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.