Can Artificial Intelligence Be Utilized to Predict Real-Time Adverse Outcomes in Individuals Arriving at the Emergency Department With Hyperglycemic Crises?

被引:0
作者
Bhimani, Alisha Amin [1 ,2 ]
Frenkel, Tova Safier [1 ,3 ]
Hasham, Adam Kaizer [4 ]
机构
[1] Emory Univ, Nell Hodgson Woodruff Sch Nursing, Atlanta, GA 30322 USA
[2] Swedish Hlth Serv, Seattle, WA USA
[3] Emory Hosp, Atlanta, GA USA
[4] Georgia Inst Technol, Atlanta, GA USA
关键词
adverse outcome; and hyperosmolar hyperglycemia state (HHS); artificial intelligence; diabetic ketoacidosis (DKA); hospital information system (HIS); hyperglycemia; machine learning; sepsis;
D O I
10.1097/TME.0000000000000508
中图分类号
R47 [护理学];
学科分类号
1011 ;
摘要
This column on translating research into practice is crafted to offer advanced practice registered nurses an analysis of current research topics that hold practical relevance for emergency care settings. The article titled "Using Artificial Intelligence to Predict Adverse Outcomes in Emergency Department Patients With Hyperglycemic Crises in Real Time," authored by C. Hsu et al. (2023), investigates through a randomized control trial, the effectiveness of artificial intelligence as a practical tool compared with the traditional predicting hyperglycemic crisis death score to clinically predict adverse outcomes in individuals presenting to the emergency department with hyperglycemic crises. The results are discussed in the context of averting adverse outcomes associated with sepsis/septic shock, intensive care unit admission, and all-cause mortality within a 1-month time frame.
引用
收藏
页码:93 / 100
页数:8
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