1 research outputs found
Interactivity and Transparency in Medical Risk Assessment with Supersparse Linear Integer Models
Scoring systems are linear classification models that only require users to
add or subtract a few small numbers in order to make a prediction. They are
used for example by clinicians to assess the risk of medical conditions. This
work focuses on our approach to implement an intuitive user interface to allow
a clinician to generate such scoring systems interactively, based on the
RiskSLIM machine learning library. We describe the technical architecture which
allows a medical professional who is not specialised in developing and applying
machine learning algorithms to create competitive transparent supersparse
linear integer models in an interactive way. We demonstrate our prototype
machine learning system in the nephrology domain, where doctors can
interactively sub-select datasets to compute models, explore scoring tables
that correspond to the learned models, and check the quality of the transparent
solutions from a medical perspective