research-article

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

Authors:

Joanne Taery Kim,

Jaegul ChooAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 25, Issue 1

Pages 299 - 309

https://doi.org/10.1109/TVCG.2018.2865027

Published: 01 January 2019 Publication History

Abstract

We have recently 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 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 methods 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 newly improved, interpretable, and interactive RNN-based model called RetainEX 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 individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract 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 that aim to design an interpretable and interactive visual analytics tool for RNNs.

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 25, Issue 1

Jan. 2019

1266 pages

ISSN:1077-2626

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1077-2626 © 2018 IEEE.

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IEEE Educational Activities Department

United States

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Published: 01 January 2019

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https://dl.acm.org/doi/10.1007/s11704-024-3735-7
ŞAHiN EArslan NÖzdemir D(2025)Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learningNeural Computing and Applications10.1007/s00521-024-10437-237:2(859-965)Online publication date: 1-Jan-2025
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https://dl.acm.org/doi/10.1109/TVCG.2023.3326943
Ouyang YWu YWang HZhang CCheng FJiang CJin LCao YLi Q(2024)Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332692930:1(1238-1248)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326929
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