A point-wise linear model reveals reasons for 30-day readmission of heart failure patients
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Heart failures in the United States cost an estimated 30.7 billion dollars annually and predictive analysis can decrease costs due to readmission of heart failure patients. Deep learning can predict readmissions but does not give reasons for its predictions. Ours is the first study on a deep-learning approach to explaining decisions behind readmission predictions. Additionally, it provides an automatic patient stratification to explain cohorts of readmitted patients. The new deep-learning model called a point-wise linear model is a meta-learning machine of linear models. It generates a logistic regression model to predict early readmission for each patient. The custom-made prediction models allow us to analyze feature importance. We evaluated the approach using a dataset that had 30-days readmission patients with heart failures. This study has been submitted in PLOS ONE. In advance, we would like to share the theoretical aspect of the point-wise linear model as a part of our study.
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