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.

中文翻译：

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逐点线性模型揭示心力衰竭患者 30 天再入院的原因

美国的心力衰竭每年花费估计 307 亿美元，预测分析可以降低因心力衰竭患者重新入院而导致的成本。深度学习可以预测再入院，但不会给出其预测的理由。我们的第一项研究采用深度学习方法来解释再入院预测背后的决策。此外，它还提供了一个自动的患者分层来解释再入院患者的队列。称为逐点线性模型的新深度学习模型是线性模型的元学习机。它生成一个逻辑回归模型来预测每个患者的早期再入院。定制的预测模型使我们能够分析特征重要性。我们使用具有 30 天再入院心力衰竭患者的数据集评估该方法。该研究已在 PLOS ONE 中提交。作为我们研究的一部分，我们想提前分享逐点线性模型的理论方面。