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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

114
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
114
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.2K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
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
The Availability Heuristic01:08

The Availability Heuristic

5.9K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
5.9K

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

Implementing EHR Suites: Lessons from a Norwegian Configuration Process.

Studies in health technology and informatics·2025
Same author

The Role of Health Informatics Research: A Case of a Large-Scale Implementation in Norway.

Studies in health technology and informatics·2024
Same author

Insights from the Implementation of Open Notes in Sweden.

Studies in health technology and informatics·2024
Same author

Technostress in Nuclear Medicine: A Qualitative Study of Causes, Mitigators, and Resolution Levels.

International journal of medical informatics·2024
Same author

Assessing metabolic risk factors for psychiatric patients: An IT-supported task shift from physician to pharmacist.

International journal of medical informatics·2024
Same author

Ugeskrift for laeger·2024
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2025

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

预测患者不出现:用不完美的数据进行定位机器学习.

Christopher Gyldenkærne1, Jakob Grue Simonsen2, Gustav From3

  • 1Department of People and Technology, Roskilde University, Denmark.

Studies in health technology and informatics
|August 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了机器学习模型来预测患者没有出现在门诊手术中,实现了高准确度. 医院人员的参与改善了模型的性能,证明了降低医疗保健成本的有希望的方法.

关键词:
机器学习是机器学习.医疗保健 医疗保健 医疗保健 医疗保健参与式设计是一种参与式设计.没有出现的患者.

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
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

相关实验视频

Last Updated: Jun 15, 2025

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
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
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

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习
  • 运营研究 运营研究

背景情况:

  • 患者不出现代表了医疗保健机构的重大财务负担和运营挑战.
  • 准确预测患者不出现的情况对于优化资源配置和改善患者流动至关重要.

研究的目的:

  • 开发和评估机器学习 (ML) 模型,用于预测患者没有出现在门诊手术中.
  • 评估定位工作和员工参与对模型性能的影响.
  • 为了确定在内镜病房中预测不显示的最佳性能ML模型.

主要方法:

  • 使用了最初未用于ML目的收集的患者历史数据.
  • 员工在医院内工作,以了解数据实践和完善模型.
  • 训练并比较各种ML模型,包括XGBoost与过量采样.
  • 使用灵敏度,特异性和准确度指标评估模型性能.

主要成果:

  • 性能最好的模型 (采用过量采样的XGBoost) 实现了0.97的灵敏度,0.66的特异性和0.95.95的准确性.
  • 定位工作和员工参与导致模型性能有显著的定量改进.
  • 开发的模型显示了对患者不出现的高预测能力.

结论:

  • 机器学习模型,特别是过量采样的XGBoost,显示出在门诊外科手术环境中预测患者不出现的巨大潜力.
  • 医院工作人员参与的协作设计对于提高ML模型的性能和适用性至关重要.
  • 虽然有希望,但这些模型对其他医院病房和机构的通用性需要进一步验证和适应.