Machine Learning Model Interpretability for Precision Medicine
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.