Automated Feature Learning: Mining Unstructured Data for Useful Abstractions

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.