Conference Posters

Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (AAIC 2019)

Charles Fisher, Yannick Pouliot, Aaron Smith, Jonathan Walsh

Objective: To develop a method to model disease progression that simulates detailed clinical data records for subjects in the control arms of Alzheimer's disease clinical trials. Methods: We used a robust data processing framework to build a machine learning dataset from a database of subjects in the control arms of a diverse set of 28 clinical trials on Alzheimer's disease. From this dataset, we selected 1908 subjects with 18-month trajectories of 44 variables and trained a model capable of simulating disease progression in 3-month intervals across all variables. Results: Based on a statistical analysis comparing data from actual and simulated subjects, the model generates accurate subject-level distributions across variables and through time. Focusing on a common clinical trial endpoint for Alzheimer's disease (ADAS-Cog), we show the model can predict disease progression as accurately as several supervised models. Our model also predicts the outcome of a clinical trial whose data are distinct from the training and test datasets. Conclusion: The ability to simulate dozens of clinical characteristics simultaneously is a powerful tool to model disease progression. Such models have useful applications for clinical trials, from analyzing control groups to supplementing real subject data in control arms.

Conference Posters

Generating Synthetic Control Subjects Using Machine Learning for Clinical Trials in Alzheimer's Disease (DIA 2019)

Charles K. Fisher, Yannick Pouliot, Aaron M. Smith, Jonathan R. Walsh

Objective: To develop a method to model disease progression that simulates detailed patient trajectories. To apply this model to subjects in control arms of Alzheimer's disease clinical trials. Methods: We used a robust data processing framework to build a machine learning dataset from a database of subjects in the control arms of a diverse set of 28 different clinical trials on Alzheimer's disease. From this dataset, we selected 1908 subjects with 18-month trajectories of 44 variables and trained 5 cross-validated models capable of simulating disease progression in 3-month intervals across all variables. Results: Based on a statistical analysis comparing data from actual patients with simulated patients, the model generates accurate patient-level distributions across variables and through time. Focusing on a common clinical trial endpoint for Alzheimer’s disease (ADAS-Cog), we show the model can predict disease progression as accurately as several supervised models. Our model also predicts the outcome of a clinical trial whose data are distinct from the training and test datasets. Conclusion: The ability to simulate dozens of patient characteristics simultaneously is a powerful tool to model disease progression. Such models have useful applications for clinical trials, from analyzing control groups to supplementing real subject data in control arms.

Biology Publications

Deep Learning of Representations for Transcriptomics-based Phenotype Prediction

Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhang, Daniel Ziemek, Charles K. Fisher

The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. This task is complicated because expression data are high dimensional whereas each experiment is usually small (e.g., ~20,000 genes may be measured for ~100 subjects). However, thousands of transcriptomics experiments with hundreds of thousands of samples are available in public repositories. Can representation learning techniques leverage these public data to improve predictive performance on other tasks? Here, we report a comprehensive analysis using different gene sets, normalization schemes, and machine learning methods on a set of 24 binary and multiclass prediction problems and 26 survival analysis tasks. Methods that combine large numbers of genes outperformed single gene methods, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses.

Biology Publications

Deep learning for comprehensive forecasting of Alzheimer's Disease progression

Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh, the Coalition Against Major Diseases

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.