Machine learning predicts who will decline faster in Alzheimer’s disease using routine clinic data

By harnessing everyday clinical assessments, researchers demonstrate that personalized 12-month forecasts of cognitive and functional change in dementia can be achieved without expensive imaging or invasive testing.

Study design and analysis pipeline. Clinical assessments are collected at regular intervals throughout the Minder study (a), features used for statistical analysis and predictive modelling included clinical assessment scores, participant demographics, and comorbidities (b), participants were first grouped based on their relative cognitive and functional decline trajectories and profiled accordingly (c), predictive models of cognitive and functional decline were fine-tuned and evaluated using a nested cross-validation approach (d), and models were selected and finalised for each outcome measure (e). Finally, a decision support tool was designed to deploy both predictive models in clinical settings (f). MMSE: Mini-mental state exam, ADAS-Cog: Alzheimer’s Disease Assessment Scale-Cognitive Subscale, BADL: Bristol Activities of Daily Living

Study design and analysis pipeline. Clinical assessments are collected at regular intervals throughout the Minder study (a), features used for statistical analysis and predictive modelling included clinical assessment scores, participant demographics, and comorbidities (b), participants were first grouped based on their relative cognitive and functional decline trajectories and profiled accordingly (c), predictive models of cognitive and functional decline were fine-tuned and evaluated using a nested cross-validation approach (d), and models were selected and finalised for each outcome…

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