使用数据分析预测败血症患者的住院死亡率

Yazan Alnsour, R. Hadidi, N. Singh
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引用次数: 2

摘要

预测分析可用于预测与某些患者相关的风险,预测模型可用于提醒医生并允许及时的主动干预。最近,医疗保健提供者一直在使用具有预测功能的不同类型的工具。脓毒症是美国乃至全世界院内死亡的主要原因之一。在这项研究中,作者使用大型医学数据集开发并提出了一个预测败血症患者住院死亡率的模型。该预测模型是使用超过100万住院患者记录的数据集开发的。使用卡方自动相互作用检测器确定住院死亡率的独立预测因子。作者发现,在预测模型中加入医院属性后,准确率从82.08%提高到85.3%,曲线下面积从0.69提高到0.84,与仅使用患者属性相比,这是有利的。作者讨论了在识别高风险患者时使用结合患者和医院属性的预测模型的实际和研究贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Data Analytics to Predict Hospital Mortality in Sepsis Patients
Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.
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