Suppr超能文献

使用NeuroShell分析与血培养阳性相关的患者参数:一项回顾性病历审查

Patient Parameters Associated With a Positive Blood Culture Using NeuroShell: A Retrospective Chart Review.

作者信息

Giovane Richard, Sheppard Robert A

机构信息

Family Medicine, University of Alabama at Birmingham, Tuscaloosa, USA.

Internal Medicine, University of Alabama at Birmingham, Tuscaloosa, USA.

出版信息

Cureus. 2022 Aug 31;14(8):e28635. doi: 10.7759/cureus.28635. eCollection 2022 Aug.

Abstract

Bacteremia is a common and life-threatening condition. It has an incidence of 140 to 160 per 100,000 person-years in the United States. Since bacteremia has many presentations, it can be challenging to diagnose. Subsequently there are very few guidelines on when to order a blood culture in an emergency setting. Neural networks are a means of machine learning and are presently being used in medicine to aid in decision making. With the use of machine learning, 22 variables that have been associated with infection and bacteremia were used to build a neural network to determine which variables associated with bacteremia are most associated with a positive blood culture.

摘要

菌血症是一种常见且危及生命的病症。在美国,其发病率为每10万人年140至160例。由于菌血症有多种表现形式,诊断可能具有挑战性。随后,关于在紧急情况下何时进行血培养的指南非常少。神经网络是机器学习的一种手段,目前正在医学中用于辅助决策。通过使用机器学习,22个与感染和菌血症相关的变量被用于构建一个神经网络,以确定哪些与菌血症相关的变量与血培养阳性最相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c844/9524715/3f59224b973b/cureus-0014-00000028635-i01.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验