Given that the rate of avoidable readmission has now develop into a key measure of the quality of care offered in a hospital, there have been more and more large variety of studies that use healthcare knowledge for understanding readmissions. Many of those strategies use machine studying methods, primarily Logistic Regression, to construct classifiers and report consistent performance. While the predictive fashions show promise, their average efficiency implies that they are still not at a stage where hospitals may use them as “black-box” decision help instruments. However, given that the main target of those predictive fashions has been on performance, the fashions themselves usually are not easily interpretable to supply actionable insights to the decision makers. In this paper, we current a technique to infer such insights from healthcare knowledge within the context of readmissions. We construct a logistic regression based mostly classifier to foretell if a patient is more likely to be readmitted primarily based on their disease history obtainable from insurance records. We use sparsity inducing regularizers in our predictive mannequin to promote interpretability.
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