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

Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K

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

External validation of the IHXGboost-P model to predict incisional hernia after midline laparotomy.

Hernia : the journal of hernias and abdominal wall surgery·2026
Same author

Identifying Key Features Associated with Excessive Fructose Intake: A Machine Learning Analysis of a Mexican Cohort.

Nutrients·2025
Same author

Prediction of in-hospital death among patients admitted to a tertiary care hospital over the first 10 years: a machine learning approach.

Frontiers in public health·2025
Same author

External validation of the ACS NSQIP surgical risk calculator for the prediction of surgical site infection (SSI) and its association with the postoperative occurrence of incisional hernia (IH) in midline laparotomy patients.

Langenbeck's archives of surgery·2025
Same author

Evaluation of the PAN-PROMISE Symptom Scale in a Randomized Controlled Trial of Fluid Resuscitation in Acute Pancreatitis.

The American journal of gastroenterology·2025
Same author

Assessment of ultraviolet radiation impact on human skin tissue using double-exposure digital holographic interferometry.

Journal of biomedical optics·2025
Same journal

Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.

BMC medical informatics and decision making·2026
Same journal

Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.

BMC medical informatics and decision making·2026
Same journal

Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.

BMC medical informatics and decision making·2026
Same journal

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BMC medical informatics and decision making·2026
Same journal

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BMC medical informatics and decision making·2026
Same journal

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

BMC medical informatics and decision making·2026
查看所有相关文章

相关实验视频

Updated: May 7, 2026

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

8.9K

使用机器学习模型预测切口.

Edgard Efren Lozada-Hernández1,2, Tania A Ramirez-DelReal3,4, Sebastián Salazar-Colores3,5

  • 1Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación (INFOTEC), Aguascalientes, 20326, México.

BMC medical informatics and decision making
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测了腹腔切割术后的切口. XGBoost模型可以识别高风险患者,改善手术结果并减少并发症.

关键词:
决策树 决策树是一个决策树.切口的切口是什么意思机器学习 机器学习中线拉皮切除术是指中线拉皮切除术.回归后勤学是一种回归后勤学.在XGBoost中使用.

更多相关视频

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

1.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

789

相关实验视频

Last Updated: May 7, 2026

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

8.9K
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

1.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

789

科学领域:

  • 手术瘤学手术瘤学
  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学

背景情况:

  • 切口 (IH) 是腹腔切除术后的常见并发症,高危人群中高达40%的患者受到影响.
  • 目前的方法缺乏共识,用于识别高风险的IH患者.
  • 机器学习 (ML) 在IH预测方面尚未得到广泛研究.

研究的目的:

  • 开发和评估用于预测中线腹腔切除术后IH的ML模型.
  • 为了确定IH发展的关键风险因素.
  • 用贝叶斯定理来评估预测模型的临床效用.

主要方法:

  • 追溯队列研究包括789名接受中线腹腔切除术的成年患者.
  • 评估了三个ML技术:物流回归,决策树和XGBoost.
  • 通过贝叶斯定理评估模型性能,使用AUC,布里埃得分和临床效用.

主要成果:

  • 在789名患者中,161名患者 (20.1%) 患上了IH.
  • 在XGBoost模型中,AUC值为0.93±0.02,Brier分数为0.10.
  • 手术前的手术部位感染风险是最强的预测因素;该模型准确地重新分类了风险概率.

结论:

  • 一个基于XGBoost的ML模型有效地预测了拉巴切除术后的IH风险.
  • 该模型表现出强度和概括潜力,通过交叉验证和学习曲线进行验证.
  • 为实际的临床使用,开发了一个可访问的Web应用程序.