  Background: Predictive modeling is a key component of solutions to many
healthcare problems. Among all predictive modeling approaches, machine learning
methods often achieve the highest prediction accuracy, but suffer from a
long-standing open problem precluding their widespread use in healthcare. Most
machine learning models give no explanation for their prediction results,
whereas interpretability is essential for a predictive model to be adopted in
typical healthcare settings. Methods: This paper presents the first complete
method for automatically explaining results for any machine learning predictive
model without degrading accuracy. We did a computer coding implementation of
the method. Using the electronic medical record data set from the Practice
Fusion diabetes classification competition containing patient records from all
50 states in the United States, we demonstrated the method on predicting type 2
diabetes diagnosis within the next year. Results: For the champion machine
learning model of the competition, our method explained prediction results for
87.4% of patients who were correctly predicted by the model to have type 2
diabetes diagnosis within the next year. Conclusions: Our demonstration showed
the feasibility of automatically explaining results for any machine learning
predictive model without degrading accuracy.
