Clinical operations staff need to have confidence in machine learning predictive models and be able to validate the accuracy of outcomes. By knowing which indicators have the most impact on these models, organizations can focus on those indicators to refine their models and learn from these insights, which can ultimately drive behavioral changes (i.e., less reliance on subjective decisions) to optimize business processes.
Machine learning allows organizations to continuously improve with direct implications on timelines and associated costs of clinical trials. The SHapley Additive exPlanations (SHAP) diagrams allow clinical operation teams to ascertain the importance of indicators, their relative weighing, and interaction. By conducting this analysis, insights and prediction with confidence is possible.