The revolution in digitizing medical data, along with high throughput biology, has resulted in unprecedented numbers of potential prognostic variables for health outcomes. Recent research suggests that clinical data can be used for continually updated accurate prognostics to optimize medical interventions. We address gaps in methodology available for estimating causal impacts in acute trauma settings using machine learning, and tailor this methodological research to the goals of using complex clinical data to analyze potential outcomes in acute trauma patients.
