Introduction
There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years.
Methods
Data from 1737 patients were processed using the “All‐Pairs” technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients).
Results
A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment.
Discussion
Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.
Introduction
This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants.
Methods
We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth.
Results
Study partner–assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization.
Discussion
Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.
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