摘要
由新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细的机器学习数据已经呈现。根据来自加州斯坦福的新闻，由新的sRx通讯员，研究表明：“目的使用机器学习来检查经历护士敏感性指标(NSI)事件的患者的健康公平和临床结果，定义为跌倒。”医院获得性压力伤害(HAPI)或医院获得性感染(HAI)设计这是一项在六个日历年内从一家学术医院进行的回顾性OBS服务研究(2/2016 021)。使用机器学习检查患有NSI的患者，并与没有患有NSI的患者进行比较。方法纳入标准：所有成年住院患者(2016-2021)。"本研究的财政支持者包括研究办公室患者护理服务，斯坦福医疗保健，专业实践和临床改进，斯坦福医疗保健，研究办公室，病人护理服务，斯坦-福特医疗保健，医学院，研究信息学中心，斯坦福大学，定量科学单位，斯坦福大学，医院质量部，斯坦福医疗保健。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news originating from Stanford, California, by New sRx correspondents, research stated, "PurposeTo use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitiv e indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).DesignThis was a retrospective obs ervational study from a single academic hospital over six calendar years (2016-2 021). Machine learning was used to examine patients with an NSI compared to thos e without.MethodsInclusion criteria: all adult inpatient admissions (2016-2021). " Financial supporters for this research include Office of Research Patient Care S ervices, Stanford Health Care, Professional Practice & Clinical Im provement, Stanford Health Care, Office of Research, Patient Care Services, Stan ford health Care, School of Medicine, Research Informatics Center, Stanford Univ ersity, Quantitative Sciences Unit, Stanford University, Hospital Quality Depart ment, Stanford Health Care.
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