openalexZenodo (CERN European Organization for Nuclear Research)Cited by 0

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.

Also available via: European Organization for Nuclear Research

View free PDFSource page

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

Related papers

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Data and code for: Frequent Mental Distress Across Texas Census Tracts: Social-Environmental Co-Exposure, Spatial Dependence, and Interpretable Machine Learning

Discriminating global ore deposit genetic types using chalcopyrite trace elements: Insights from interpretable machine learning

Dictionary of Applied Machine Learning (Course Edition)

An Aromatic-Vinylene Structural Correlate of CO₂ Permeability in Glassy Polymer Membranes Identified by Interpretable Machine Learning

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches