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In biomedical data analysis, inferring the cause of death is a challenging and important task, which is useful for both public health reporting purposes, as well as improving patients' quality of care by identifying severer conditions. Causal inference, however, is notoriously difficult. Traditional causal inference mainly relies on analyzing data collected from experiment of specific design, which is expensive, and limited to a certain disease cohort, making the approach less generalizable. In our paper, we adopt a novel data-driven perspective to analyze and improve the death reporting process, to assist physicians identify the single underlying cause of death. To achieve this, we build state-of-the-art deep learning models, convolution neural network (CNN), and achieve around 75% accuracy in predicting the single underlying cause of death from a list of relevant medical conditions. We also provide interpretations for the black-box neural network models, so that death reporting physicians can apply the model with better understanding of the model.

acm-bcb ... ... : the ... acm conference on bioinformatics, computational biology and biomedicine. acm conference on bioinformatics, computational biology and biomedicine

Infer Cause of Death for Population Health Using Convolutional Neural Network