KARMA constructs minimal-K Markov transition kernels as surrogates to deliver global explanations for multivariate time series forecasting models and recovers known causal structure on synthetic data.
Clinical Intervention Prediction and Understanding using Deep Networks
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abstract
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs). This task is complicated by data sources that are noisy, sparse, heterogeneous and outcomes that are imbalanced. In this paper, we integrate data from all available ICU sources (vitals, labs, notes, demographics) and focus on learning rich representations of this data to predict onset and weaning of multiple invasive interventions. In particular, we compare both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five intervention tasks: invasive ventilation, non-invasive ventilation, vasopressors, colloid boluses, and crystalloid boluses. Our predictions are done in a forward-facing manner to enable "real-time" performance, and predictions are made with a six hour gap time to support clinically actionable planning. We achieve state-of-the-art results on our predictive tasks using deep architectures. We explore the use of feature occlusion to interpret LSTM models, and compare this to the interpretability gained from examining inputs that maximally activate CNN outputs. We show that our models are able to significantly outperform baselines in intervention prediction, and provide insight into model learning, which is crucial for the adoption of such models in practice.
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Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations
KARMA constructs minimal-K Markov transition kernels as surrogates to deliver global explanations for multivariate time series forecasting models and recovers known causal structure on synthetic data.
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Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Delta-XAI wraps existing XAI methods for online time series and introduces SWING to explain prediction changes while accounting for temporal dependencies.
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