OBJECTIVES Our goal was to develop predictive models for sepsis and in-hospital mortality using electronic health records (EHRs). We showcased the efficiency of these algorithms in patients diagnosed with pneumonia, a group that is highly susceptible to sepsis. METHODS We retrospectively analyzed the Health Facts® (HF) dataset to develop models to predict mortality and sepsis using the data from the first few hours after admission. In addition, we developed models to predict sepsis using the data collected in the last few hours leading to sepsis onset. We used the random forest classifier to develop the models. RESULTS The data collected in the EHR system is generally sporadic, making feature extraction and selection difficult, affecting the accuracies of the models. Despite this fact, the developed models can predict sepsis and in-hospital mortality with accuracies of up to 65.26±0.33% and 68.64±0.48%, and sensitivities of up to 67.24±0.36% and 74.00±1.22%, respectively, using only the data from the first 12 hours after admission. The accuracies generally remain consistent for similar models developed using the data from the first 24 and 48 hours after admission. Lastly, the developed models can accurately predict sepsis patients (with up to 98.63±0.17% accuracy and 99.74%±0.13% sensitivity) using the data collected within the last 12 hours before sepsis onset. The results suggest that if such algorithms continuously monitor patients, they can identify sepsis patients in a manner comparable to current screening tools, such as the rulebased Systemic Inflammatory Response Syndrome (SIRS) criteria, while often allowing for early detection of sepsis shortly after admission. CONCLUSIONS The developed models showed promise in early prediction of sepsis, providing an opportunity for directing early intervention efforts to prevent/treat sepsis.

中文翻译：

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使用电子健康记录预测败血症和医院内死亡率

目标我们的目标是使用电子健康记录（EHR）开发败血症和医院内死亡率的预测模型。我们展示了这些算法在被诊断为高度易患败血症的肺炎患者中的有效性。方法我们回顾性分析了HealthFacts®（HF）数据集，以使用入院后最初几个小时的数据开发模型来预测死亡率和败血症。此外，我们开发了使用过去数小时内导致败血症发作的数据来预测败血症的模型。我们使用随机森林分类器来开发模型。结果EHR系统中收集的数据通常是零星的，这使得特征提取和选择变得困难，从而影响了模型的准确性。尽管有这个事实，仅使用第一个数据，开发的模型可以预测败血症和住院死亡率，其准确度分别高达65.26±0.33％和68.64±0.48％，敏感性分别高达67.24±0.36％和74.00±1.22％。入院后12小时。对于使用入院后最初24和48小时的数据开发的类似模型，其准确性通常保持一致。最后，开发的模型可以使用败血症发作前最后12个小时内收集的数据准确预测败血症患者（准确率高达98.63±0.17％，敏感性为99.74％±0.13％）。结果表明，如果此类算法持续监控患者，则他们可以以与当前筛查工具（例如基于规则的系统性炎症反应综合征（SIRS）标准）相媲美的方式识别败血症患者，同时通常允许在入院后不久就对败血症进行早期检测。结论所开发的模型在脓毒症的早期预测中显示出希望，为指导早期干预措施预防/治疗脓毒症提供了机会。