AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator
Feng Xie, Yilin Ning, Han Yuan, M. Liu, Siqi Li, Seyed Ehsan Saffari, Bibhas Chakraborty, Nan Liu · 2021
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. 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.