首页|New Machine Learning Findings from Stanford Health Care Described (Machine Learn ing Evaluation of Inequities and Disparities Associated With Nurse Sensitive Ind icator Safety Events)
New Machine Learning Findings from Stanford Health Care Described (Machine Learn ing Evaluation of Inequities and Disparities Associated With Nurse Sensitive Ind icator Safety Events)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.
StanfordCaliforniaUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesHospitalsMachine Learn ingRisk and PreventionStanford Health Care
NETL
NSTL
