It is not easy to understand your question for me...
I think the purpose of risk estimation by machine learning is not identification of risk factors. But I want to find risk factors, too.
The picture I attached is from articles of RETAIN (arXiv:1608.05745 [cs]), which used deep learning (RNN) with attention model. They tried to predict heart failure. In this picture, you can see that arrhythmia, athersclerosis and heart valve disorder is strong risk factors for HF. What is interesting is that anticoagulant medication and antiarrhythmic medication seems to have negative association with development of HF in the same patient (maybe preventing progression of coronary atherosclerosis).
Like this, I believe we can show that which factors confers benefit and risk for the patient by using machine learning. Moreover, I want to show the association between progression of underlying risk factors and the disease (such as relationship between progression of DM and the development of myocardial infarction). This is one of what I want to develop.
As I said, I didn't fully understand your question.. So I'm not sure this is the answer you want.