一种无监督的错误检测方法,用于检测医疗保健分析中的错误标签
Pei-Yuan Zhou1, Faith Lum1, Tony Jiecao Wang1
1Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Bioengineering (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种无监督的方法,用于检测医疗数据集中的异常样本. 模式发现和解 (PDD) 模型提高了数据质量,提高了临床决策和分类准确性.
科学领域:
- 医疗保健数据分析数据分析
- 机器学习在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 医疗数据集经常遭受不平衡的类和主观测试和临床变异性错误的错误.
- 数据质量不佳会对分类准确性和可靠性产生负面影响,阻碍有效的临床决策.
研究的目的:
- 提出一种无监督的错误检测方法,以提高医疗数据集的质量.
- 利用模式发现和解 (PDD) 模型来识别和删除异常样本.
主要方法:
- 利用模式发现和解 (PDD) 模型在大型数据集中发现统计学上显著的关联模式.
- 将算法应用于eICU协作研究数据库进行败血症风险评估.
- 无监督地集群样本并检测出异常数据点.
主要成果:
- 拟议的算法在完整数据集上表现优于K-Means集群,在完整数据集上表现优于38%,在减少数据集上表现优于47%.
- 通过使用错误检测方法去除异常样本,多个监督分类器的准确性平均提高了4%.
结论:
- 开发的算法为无监督的集群和医疗数据错误检测提供了强大而实用的解决方案.
- 通过自动错误检测来提高数据质量,可以显著提高临床风险评估和预测建模的可靠性.
相关概念视频
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...
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
Errors occurring during blood pressure monitoring
621
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
Several factors...
621
Detection of Gross Error: The Q Test
5.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.7K
Types of Errors: Detection and Minimization
1.5K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.5K
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...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Systematic Error: Methodological and Sampling Errors
1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K


