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A self-adaptive 30-day diabetic readmission prediction model based on incremental learning

机译：基于增量学习的自适应30天糖尿病患者再入院预测模型

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Hospital readmissions within 30 days after discharge are costly and it has been a prior for researchers to identify patients at risk of early readmission. Most of the reported hospital readmission prediction models have been built with historical data and thus can outdate over time. In this work, a self-adaptive 30-day diabetic hospital readmission prediction model has been developed. A diabetic inpatient encounter data stream was used to train the self-adaptive models based on incremental learning algorithms. The result indicated that the model can automatically adapt to the newly arrived data. The best model achieved an average AUC of 0.655 ± 0.078, which is consistent with static models built with the same dataset.
机译：出院后30天内住院再住院的费用很高，研究人员发现具有早期再入院风险的患者已经成为先验。大多数报告的医院再入院预测模型都是使用历史数据构建的，因此随着时间的推移可能会过时。在这项工作中，已经开发了一种自适应的30天糖尿病医院再入院预测模型。糖尿病住院患者相遇数据流用于基于增量学习算法训练自适应模型。结果表明该模型可以自动适应新到达的数据。最佳模型的平均AUC为0.655±0.078，这与使用相同数据集构建的静态模型是一致的。

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