AutoScore - An Interpretable Machine Learning-Based Automatic Clinical Score
Generator
A novel interpretable machine learning-based framework to
automate the development of a clinical scoring model for
predefined outcomes. Our novel framework consists of six
modules: variable ranking with machine learning, variable
transformation, score derivation, model selection, domain
knowledge-based score fine-tuning, and performance
evaluation.The details are described in our research
paper<doi:10.2196/21798>. Users or clinicians could seamlessly
generate parsimonious sparse-score risk models (i.e., risk
scores), which can be easily implemented and validated in
clinical practice. We hope to see its application in various
medical case studies.