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首页> 外文会议>GMDS 2018, Annual Meeting >Prediction of Readmissions in the German DRG System Based on §21 Datasets
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Prediction of Readmissions in the German DRG System Based on §21 Datasets

机译：基于§21数据集的德国DRG系统入伍预测

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Hospital readmissions receive increasing interest, since they are burdensome for patients and costly for healthcare providers. For the calculation of reimbursement fees, in Germany there is the German-Diagnosis Related Groups (G-DRG) system. For every hospital stay, data are collected as a so-called "case", as the basis for the subsequent reimbursement calculations ("§21 dataset"). Merging rules lead to a loss of information in §21 datasets. We applied machine learning to §21 datasets and evaluated the influence of case merging for the resulting accuracy of readmission risk prediction. Data from 478,966 cases were analysed by applying a random forest. Many cases with readmissions within 30 days had been merged and thus their prediction required additional data. Using 10-fold cross validation, the prediction for readmissions within 31 — 60 days showed no notable difference in the area under the ROC curves comparing unedited §21 datasets with §21 datasets with restored original cases. The achieved AUC values of 0.69 lie in a similar range as the values of comparable state-of-the-art models. We conclude that dealing with merged cases, i.e. adding data, is required for 30-day-readmission prediction, whereas un-merging brings no improvement for the readmission prediction of period beyond 30 days.
机译：医院入院接受越来越令人利益，因为它们对患者的繁重，并且昂贵的医疗保健提供者。为了计算报销费，在德国有德国诊断相关群体（G-DRG）系统。对于每个住院住宿，数据被收集为所谓的“案例”，作为后续偿还计算的基础（“§21dataset”）。合并规则导致§21数据集中的信息丢失。我们将机器学习应用于§21数据集，并评估案例合并的影响，从而实现了再入伍风险预测的准确性。通过应用随机森林来分析来自478,966例的数据。在30天内的许多方便案件已被合并，因此它们的预测需要额外的数据。使用10倍的交叉验证，31-60天内的阅览中的预测在ROC曲线下的区域内没有显着差异，比较未定期的§21数据集与§21数据集具有恢复原始案例。实现的AUC值为0.69位于与可比最先进模型的值相似的范围内。我们得出结论，处理合并案件，即30天读入预测，否则不需要改善30天内的再次入住期限。

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