When the amount of training data is limited, the successful application of machine learning techniques typically hinges on the ability to identify useful features or abstractions. Expert knowledge often plays a crucial role in this feature engineering process. However, manual creation of such abstractions can be labor intensive and expensive. In this paper, we propose a feature learning framework that takes advantage of the vast amount of expert knowledge available in unstructured form on the Web. We explore the use of unsupervised learning techniques and non-Euclidean distance measures to automatically incorporate such expert knowledge when building feature representations. We demonstrate the utility of our proposed approach on the task of learning useful abstractions from a list of over two thousand patient medications. Applied to three clinically relevant patient risk stratification tasks, the classifiers built using the learned abstractions outperform several baselines including one based on a manually curated feature space.
['Abhishek Bafna', 'Jenna Wiens']
Automated Feature Learning: Mining Unstructured Data for Useful Abstractions