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相关概念视频

Classification of Illness01:17

Classification of Illness

8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

3.6K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
3.6K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

3.5K
A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
3.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

541
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
541
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

464
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
464
Survival Tree01:19

Survival Tree

374
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
374

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相关实验视频

Updated: Jan 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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一个树结构的多目标优化框架,用于构建与诊断相关的群组.

Gaocheng Cai1, Zhimei Zeng1, Mengjie Wan1

  • 1College of Management, Shenzhen University, Shenzhen, Guangdong, China.

NPJ digital medicine
|November 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了建立诊断相关小组 (DRG) 的新框架,以改善医疗保险支付. 该方法提高了反映患者复杂性的准确性,同时遵守分组规则.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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相关实验视频

Last Updated: Jan 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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科学领域:

  • 医疗保健服务研究 医疗服务研究
  • 卫生经济学 卫生经济学
  • 医疗信息学 医疗信息学

背景情况:

  • 诊断相关组 (DRG) 对于医疗保险支付标准至关重要.
  • 目前的DRG构建方法与分组规则违规和多目标优化失衡作斗争.
  • 这些限制阻碍了支付标准准确反映临床复杂性.

研究的目的:

  • 提出一种新的多限制多目标优化模型和DRG构建的树结构框架.
  • 解决现有DRG方法的挑战,包括遵守规则和客观平衡.
  • 开发一个DRG系统,准确地反映临床复杂性,并支持强大的支付标准.

主要方法:

  • 开发了一个数学模型,在分组约束下定义集团内部同质性和集团内部异质性目标.
  • 使用非负的自适应LASSO回归来精确量化临床复杂性.
  • 集成的树结构与多目标优化算法来生成帕雷托最佳的DRG集.

主要成果:

  • 拟议的框架成功地满足了预定义的分组约束.
  • 该方法在准确反映DRG中的临床复杂性方面表现出有效性.
  • 经验结果验证了该框架能够生成高效且符合DRG的集的能力.

结论:

  • 新的框架为构建遵守规则并优化目标的DRG提供了一个范式.
  • 这种方法为决策者提供了高效的DRG套件,以改善医疗保险支付标准.
  • 该研究推进了DRG方法,以更好地与临床现实和经济原则保持一致.