Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Discharge Summary Forms01:31

Discharge Summary Forms

742
The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
742
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Planning Nursing Care I01:21

Planning Nursing Care I

4.3K
The planning phase of the nursing process helps nurses set priorities, outline patient-centered goals and expected outcomes, and tailor nursing interventions to align with the aligned care plan. Through the planning phase, the nurse applies critical thinking skills to align and develop interventions according to the patient's needs. It provides continuity of care allowing patients to receive the maximum benefit from treatment. It serves as a pilot plan for allocating individual staff to a...
4.3K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

559
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
559

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

Electric Field Driven Soft Morphing Matter.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals.

Computers in biology and medicine·2025
Same author

Monolithic electrostatic actuators with independent stiffness modulation.

Nature communications·2025
Same author

Alternate Level of Care Patients in Canada: a Scoping Review.

Canadian geriatrics journal : CGJ·2024
Same author

Knowledge-graph-based explainable AI: A systematic review.

Journal of information science·2024
Same author

A systematic literature review of predicting patient discharges using statistical methods and machine learning.

Health care management science·2024
Same journal

What do aged care leaders need to meet quality and safety challenges?

BMC health services research·2026
Same journal

Trends and cross-county disparities in childhood pneumococcal vaccination in Zhejiang Province, China, 2011-2022.

BMC health services research·2026
Same journal

Silence, stigma, and sexual health: experiences of sexual dysfunction among men living with diabetes in Ghana.

BMC health services research·2026
Same journal

Navigating the AI era in dietetics: a qualitative analysis of professional identity, ethical concerns, and future projections in Türkiye.

BMC health services research·2026
Same journal

Availability of adolescent mental, sexual and reproductive health services (ASRH) and technical efficiency of ASRH service delivery in Niger.

BMC health services research·2026
Same journal

Prolonged return to work and hampered work ability: insights from a scoping review on the impact of long COVID on healthcare workers job performance.

BMC health services research·2026
查看所有相关文章

相关实验视频

Updated: Jun 3, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

开发一个决策支持工具,用机器学习来预测医院出院延迟情况.

Mahsa Pahlevani1, Enayat Rajabi2, Majid Taghavi3,4

  • 1Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.

BMC health services research
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测入院时的替代护理水平 (ALC) 患者,改善医院流动. 关键预测因素包括诊断,年龄和输入代码,使早期识别和资源规划成为可能.

关键词:
患有ALC的患者.延迟放电是什么意思准备放管计划 计划放管计划排放预测的预测.机器学习是机器学习.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

7.0K

相关实验视频

Last Updated: Jun 3, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

8.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

7.0K

科学领域:

  • 医疗保健管理的管理
  • 机器学习在医学中的应用
  • 医疗信息学 医疗信息学

背景情况:

  • 由于需求的增加,医疗保健系统在患者流量管理方面面临挑战.
  • 替代护理水平 (ALC) 患者不再需要急性护理,但面临出院延误,导致医院过度拥挤.
  • 在入院时对ALC患者的早期预测对于有效的资源规划和改善患者流动至关重要.

研究的目的:

  • 为了确定可能在入院时成为替代护理水平 (ALC) 的患者.
  • 确定预测ALC状态的关键特征.
  • 为在入院时早期识别ALC患者制定指导方针.

主要方法:

  • 利用了新斯科舍省卫生局 (2015-2022) 的患者数据,包括人口统计,诊断和临床信息.
  • 应用数据预处理技术,如异常值管理,特征工程,缺失值赋值和标准化.
  • 采用机器学习分类器,包括随机森林,人工神经网络和极端梯度增强 (XGB),使用类权重,随机过量抽样和SMOTE等技术来处理数据不平衡.

主要成果:

  • 使用SMOTE的XGB模型表现出卓越的性能,在ALC患者鉴定中实现了0.95的回忆率和0.97的AUC.
  • 即使仅限于入场特征时,XGB模型与SMOTE保持了强大的预测能力 (召回0.91,AUC0.94).
  • 确定ALC状态的最重要的预测因素是诊断1,患者年龄和入口代码.

结论:

  • 机器学习模型在入院时有效预测替代护理水平 (ALC) 状态,支持实时决策.
  • 该研究为早期ALC识别提供了一个框架,将预测分类为概率范围,以指导干预.
  • 实施这些预测模型可以显著改善患者流动,缓解医院过度拥挤.