Conference Posters

Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (JSM 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 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 probabilistic generative model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate 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 accurately predict disease progression and may be used to model the control arm 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 actual subject data in control arms.

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.