Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Determining the Organizational Culture and Readiness for Evidence-Based Practice Amongst Surgical Ward Nurses in Namibia: A Cross-Sectional Study.

Health science reports·2026

Same author

From policy to practice: How frontline nurses navigate disaster preparedness in a South African public hospital.

International journal of Africa nursing sciences·2026

Same author

Considering internal conflict in the face of natural product biosynthesis and biosynthetic gene cluster evolution.

Essays in biochemistry·2026

Same author

Cervical Spine Immobilisation Following Stab Wounds to the Neck Is Unnecessary.

Emergency medicine Australasia : EMA·2026

Same author

The maternal health knowledge of pregnant women attending the antenatal clinics in the Western Cape: A cross-sectional study.

Journal of public health in Africa·2026

Same author

Utilisation of digital educational technology amongst undergraduate nursing students in the Western Cape.

Health SA = SA Gesondheid·2026

Search research articles

相关实验视频

Updated: May 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

使用机器学习改善南非外科病人的再入院风险

Umit Tokac¹, Jennifer Chipps², Petra Brysiewicz³

¹College of Nursing, University of Missouri-St. Louis, St. Louis, MO 63121, USA.

International journal of environmental research and public health

|April 16, 2025

概括

这项研究开发了一种机器学习模型,用于使用自由文本电子健康记录预测南非非非计划性患者再入院. 该模型提高了预测准确度,识别了手术和创伤再入院的关键因素.

关键词:

南非南非南非机器学习是机器学习.手术手术手术手术手术手术手术一个创伤的创伤创伤.不规划的再接收.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

相关实验视频

Last Updated: May 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
公共卫生公共卫生

背景情况:

无计划的30天再接收是一个重大的全球和南非医疗保健挑战.
预测再入院对于改善患者的治疗结果和优化公共医院的资源配置至关重要.

研究的目的:

开发和评估一种机器学习模型,用于预测非计划的外科手术和创伤再入院.
利用电子健康记录中的非结构化文本数据进行再接收预测.

主要方法:

患者记录的回顾性队列分析.
应用随机森林分析与自然语言处理 (NLP) 和情绪分析相结合.
从电子注册表中的自由文本数据中提取见解.

主要成果:

曲线下的实现面积 (AUC) 值从0.54到0.92不等,与全球基准一致.
确定出院计划得分是创伤再入院的主要预测因素.
确定问题得分是手术再入院的主要预测因素.

结论:

机器学习和NLP技术提高了预测计划外患者再入院的准确性.
特定的预测变量 (出院计划,问题得分) 对不同患者队列 (创伤,手术) 至关重要.
这种方法为南非公共医院提供了一种有价值的工具,可以主动管理再接收风险.