RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records


In the past decade, we have seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients’ diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often very challenging for users to understand why the model makes a particular prediction. Such black box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established method to interactively leverage users’ domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a recently proposed, interpretable RNN-based model called RETAIN and visualizations for users’ exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how RNN models EMR data, using real medical records of patients with heart failure, cataract, or dermatological symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers who aim to design more interpretable and interactive visual analytics tool for RNNs.


Bum Chul Kwon is a Research Staff Member at IBM Research, where he is a member of the AI for Healthcare team. He is also an Adjunct Lecturer in the School of Professional Studies at Columbia University. His research goal is to enhance users’ abilities to derive knowledge from data using interactive visual analytics systems. His work has been published at premier venues in visualization and human-computer interaction, such as IEEE InfoVis, IEEE VAST, TVCG, ACM SIGCHI. He also serves on the program committee for top-tier conferences and workshops, including IEEE InfoVis, PacificVis, and Visual Analytics in Healthcare Workshop. Prior to joining IBM Research, he worked as a postdoctoral researcher in University of Konstanz, Germany. He earned his PhD and MS in Industrial Engineering from Purdue University, West Lafayette, Indiana, and his BS from University of Virginia, Charlottesville, Virginia.