Causal Phenotype Discovery via Deep Networks

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[269] first create a deep neural network to model clinical time series and then analyze the relationship of the hidden features to the output using a causal approach.”
Interpretable Deep Models for ICU Outcome Prediction (2016, methodology): “Among these methods, deep learning models (e.g., multilayer neural networks) have achieved the state-of-the-art performance on several tasks, such as computational phenotype discovery 9,10 and predictive modeling 8,11 .”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “when combined with reinforcement learning. For example, Kale et al. demonstrated how this type of deep model can be used for discovery and analysis of causal phenotypes from clinical time series data [99]. Deep neural networks trained on EHR data with temporally dependent constraints and outputs hav”
Causal Induction from Visual Observations for Goal Directed Tasks (2019, background): “Pioneer work on causal discovery with deep networks has applied to time series data in healthcare domains (Kale et al., 2015; Nauta et al., 2019).”