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Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches., (©2021 AMIA - All rights reserved.)

Huntington's Disease (HD) is a neurodegenerative disorder with serious motor, cognitive, and behavioral symptoms. Chorea, a motor symptom of HD characterized by abrupt involuntary movements, is typically treated with tetrabenazine or certain off-label antipsychotics. Clinical trial evidence about the impact of these drugs in the HD population is scant. However, multiple observational HD registries have recently been used with success to model HD progression 1,2 and provide an opportunity to obtain effect estimates in the absence of clinical trials. We use a dataset integrated from four large-scale HD registries to generate evidence on the efficacy of chorea treatments for chorea as well as their impact on other aspects of HD progression. Clinical conclusions are meant only to illustrate our methodological approach. We employ parametric G-computation for causal inference to adjust for confounding and accommodate irregular visits and treatment patterns. We fit Bayesian hierarchical models to the results of multiple related analyses to share strength across studies and handle multiple comparisons concerns.