Applied Machine Learning for CNS Clinical Trial Risk Assessment: An Interpretable Framework with an ALS Case Study

This technical report presents an applied framework for assessing risk in central nervous system (CNS) clinical trials, emphasizing interpretable, structured decision-making using biological, clinical, and operational signals. The platform integrates curated trial metadata, biomarker information, and machine learning to flag potential trial risks before execution. A focused amyotrophic lateral sclerosis (ALS) case study illustrates how trial design decisions such as biomarker use, patient stratification, and endpoint selection are translated into explicit risk factors. The report includes baseline scoring, preliminary machine learning evaluation, decision scenarios, and responsible use considerations.