Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we show that complex models such as random forest can be made interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality with 80% balanced accuracy and were also were able to interpret the relative effect of the features on prediction at individual level.
@article{arxiv.1610.09045,
title = {Machine Learning Model Interpretability for Precision Medicine},
author = {Gajendra Jung Katuwal and Robert Chen},
journal= {arXiv preprint arXiv:1610.09045},
year = {2016}
}