What is Causal Machine Learning?
With the rise of big data, machine learning emerged as an important predictive tool in modern healthcare analytics and bioinformatics. The idea of machines learning from data they consume to make predictions isn’t new. What is new is the inference of causal mechanisms from patient data that enables “what if?” simulations of a variety of treatments and interventions across individual patients to determine optimal therapies.
GNS moves beyond analytical and machine learning approaches that rely on data correlations to match treatments to patients. We reverse engineer the complex causal mechanisms that determine which therapies will produce the best outcomes for each patient. We call this approach causal machine learning and it helps us unlock the full potential of the data and get at the underlying causal mechanisms of the “system” that produced the data.
The Importance of Data Integration
- Genomic and other -omic [gene expression, epigenomic, proteomic, metabolomics]
- Molecular and laboratory
- Electronic medical records (EMR)
- Patient registry
- Consumer demographic and behavioral
- Pharmacy and medical claim
- Features extracted from imaging and natural language
- Emerging, novel, real-world, such as mobile health
No one knows healthcare data more intimately than we do. To get the full value from all your data, there simply is no better partner.