@文章{info:doi/10.2196/13719，作者=“叶承银与王，Oliver与刘，Modi与郑，乐与夏，敏杰与郝，时英与金，波与金，华与朱，春青与黄，荣超与高，彭与埃尔罗特，格雷与布伦南，丹尼与斯特恩斯，弗兰克与西尔威斯特，卡尔G与盖德，埃里克与麦克埃尔亨尼，多夫B与凌，雪峰”，标题=“住院患者死亡风险实时预警监测系统:“利用电子病历数据的前瞻性研究”，期刊=“J Med Internet Res”，年=“2019”，月=“7”，日=“05”，卷=“21”，号=“7”，页=“e13719”，关键词=“住院患者;死亡率;风险评估;电子健康记录;背景:观察到的一些住院患者病情的快速恶化可以归因于疾病进展或入院后不完善的分诊和护理分配水平。早期预警系统(EWS)可以识别随后可能发生院内死亡的高风险患者，是确保患者安全和护理质量、减少可避免的伤害和成本的有效工具。目的:本研究的目的是前瞻性验证实时EWS的设计，以预测住院期间住院死亡的高风险患者。方法:数据收集自伯克希尔卫生系统两家急症医院的全系统电子病历(EMR)，包括2015年1月1日至2017年9月30日的54,246例住院患者，其中2.30例{\%}(1248/54,246)导致院内死亡。对多种机器学习方法(线性和非线性)进行了探索和比较。 The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. Results: The EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69{\%} (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3{\%} (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions: In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities. ", issn="1438-8871", doi="10.2196/13719", url="//www.mybigtv.com/2019/7/e13719/", url="https://doi.org/10.2196/13719", url="http://www.ncbi.nlm.nih.gov/pubmed/31278734" }