Attentive State-Space Modeling of Disease Progression

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Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019, methodology): “In contrast with other examples of attention-based interpretability [1, 25, 33], which zoom in on interesting but instance-specific examples, we note that our methods focus on ways to aggregate the patterns across the entire dataset – allowing us to extract generalizable insights about temporal…”
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2020, methodology): “…data common in this setting, employing mixtures of Gaussian processes (Alaa et al. 2017), recurrent neural networks (Lee et al. 2019), state-space models (Alaa et al. 2019), neural network-based point processes (Qian et al. 2020), Bayesian non-parametric approaches (Bellot and Schaar 2020).”
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding (2021, background): “…similar underlying disease mechanisms from temporal clinical data (Lasko et al., 2013; Suresh et al., 2018; Schulam et al., 2015), and disease progression modeling, for learning the hidden vector of comorbidities representing a disease over time (Wang et al., 2014; Alaa & van der Schaar, 2019).”