iMet: Interactive Metric Learning in Healthcare Applications

Patient similarity assessment aims at providing a clinically meaningful distance measure for case retrieval in the context of clinical decision intelligence. Two of the key challenges are how to incorporate physician feedback with regard to the retrieval results and how to interactively update the underlying similarity measure based on the feedback. In this paper, we present the interactive Metric learning (iMet) method that can incrementally adjust the underlying distance metric based on latest supervision information. iMet is designed to scale linearly with the data set size based on matrix perturbation theory which allows the derivation of sound theoretical guarantees. We show empirical results demonstrating that iMet outperforms the baseline by three orders of magnitude in speed while obtaining comparable accuracy on several benchmark datasets. We also describe the application of the algorithm in a real world physician decision support system.