Deep learning for comprehensive forecasting of Alzheimer's Disease progression

Abstract

Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics, generating both predictions and their confidence intervals. Our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models and identifies sub-components associated with word recall as predictive of progression. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease.

References in corpus (2)

• Conditional Restricted Boltzmann Machines for Structured Output Prediction
• Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease

Cited by in corpus (12)

• Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey
• Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
• Interpretable machine learning for high-dimensional trajectories of aging health
• The potential for complex computational models of aging
• Dementia Prediction Applying Variational Quantum Classifier
• Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
• A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease
• Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins
• Predicting Progression Events in Multiple Myeloma from Routine Blood Work
• Patient Similarity Analysis with Longitudinal Health Data
• Methods for generating and evaluating synthetic longitudinal patient data: a systematic review
• Hierarchical Dynamic Modeling for Individualized Bayesian Forecasting