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Deep Recurrent Neural Networks (RNN) continues to find success in predictivedecision-making with temporal event sequences. Recent studies have shown theimportance and practicality of visual analytics in interpreting deep learningmodels for real-world applications. However, very limited work enablesinteractions with deep learning models and guides practitioners to formhypotheticals towards the desired prediction outcomes, especially for sequenceprediction. Specifically, no existing work has addressed the what-if analysisand value perturbation along different time-steps for sequence outcomeprediction. We present a model-agnostic visual analytics tool, HypperSteer,that steers hypothetical testing and allows users to perturb data for sequencepredictions interactively. We showcase how HypperSteer helps in steeringpatient data to achieve desired treatment outcomes and discuss how HypperSteercan serve as a comprehensive solution for other practical scenarios.

HypperSteer: Hypothetical Steering and Data Perturbation in Sequence Prediction with Deep Learning