A1700
Title: Interpretable machine learning-based scoring system for clinical decision making
Authors: Nan Liu - National University of Singapore (Singapore) [presenting]
Abstract: There has been an increased use of scoring systems in clinical settings to assess risks in a convenient manner that provides important evidence for decision-making. Machine learning-based methods may be useful for identifying important predictors and building models; however, their 'black box' nature limits their interpretability as well as clinical acceptability. The aim is to introduce and demonstrate how interpretable machine learning can be used to create scoring systems for clinical decision-making.
