4.6 Article

Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study

Related references

Note: Only part of the references are listed.
Article Medical Laboratory Technology

Traceable machine learning real-time quality control based on patient data

Rui Zhou et al.

Summary: The study aimed to develop a patient-based real-time quality control model using machine learning, and compared it with traditional methods for clinical validity evaluation. The results showed that the machine learning model performed superiorly to traditional methods under critical bias.

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2022)

Article Multidisciplinary Sciences

A study on quality control using delta data with machine learning technique

Yufang Liang et al.

Summary: This study proposes a novel protocol for improving the capacity of QC event detection by combining delta data with machine learning technique.

HELIYON (2022)

Article Biology

A multi-model fusion algorithm as a real-time quality control tool for small shift detection

Rui Zhou et al.

Summary: This study aims to establish an artificial intelligence-based quality control method for small error detection in real laboratory settings, focusing on tPSA. By extracting data, simulating data, and constructing models, a fusion algorithm with outstanding performance was developed.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Medical Laboratory Technology

Planning SQC strategies and adapting QC frequency for patient risk

James O. Westgard et al.

Summary: This study introduces a method for planning SQc strategies using risk-based statistical quality control strategies, considering patient risk and adjusting run size to accommodate the desired reporting intervals of the laboratory. Using examples from HbA1c testing and an 18-test chemistry analyzer, the study illustrates achieving quality goals at different control levels and adjusting run size to fit the patient risk of different tests.

CLINICA CHIMICA ACTA (2021)

Article Medical Laboratory Technology

Impact of combining data from multiple instruments on performance of patient-based real-time quality control

Qianqian Zhou et al.

Summary: Optimizing and individually applying the PBRTQC algorithm to data from individual simulated instruments enables faster bias detection compared to when the algorithm is applied to combined data, potentially missing smaller biases. The individually applied algorithm shows more consistent error detection rates across different magnitudes of bias compared to the algorithm applied on combined data.

BIOCHEMIA MEDICA (2021)

Article Medical Laboratory Technology

Regression-Adjusted Real-Time Quality Control

Xincen Duan et al.

Summary: Patient-based real-time quality control (PBRTQC) has been questioned for its performance and practical applicability in some analytes, leading to the introduction of a new method called regression-adjusted real-time quality control (RARTQC) to improve the protocols. The study compared the performance of PBRTQC and RARTQC using patient test results from Zhongshan Hospital, Fudan University, in 2019, and found that RARTQC outperformed PBRTQC by improving the trimmed average number of patients affected before detection by about 50% for both constant and proportional errors. RARTQC, with its regression step, removes autocorrelation in test results, allows additional variables, and improves data transformation, making it a powerful framework for real-time quality control research.

CLINICAL CHEMISTRY (2021)

Review Medical Laboratory Technology

Recommendation for performance verification of patient-based real-time quality control

Tze Ping Loh et al.

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2020)

Article Medical Laboratory Technology

Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling

Andreas Bietenbeck et al.

CLINICAL CHEMISTRY (2020)

Article Medical Laboratory Technology

Design and implementation of quality control plans that integrate moving average and internal quality control: incorporating the best of both worlds

Huub H. van Rossum et al.

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2019)

Review Medical Laboratory Technology

Patient-Based Real-Time Quality Control: Review and Recommendations

Tony Badrick et al.

CLINICAL CHEMISTRY (2019)

Review Medical Laboratory Technology

Moving average quality control: principles, practical application and future perspectives

Huub H. van Rossum

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2019)

Article Medical Laboratory Technology

Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision

Jiakai Liu et al.

CLINICAL BIOCHEMISTRY (2018)

Article Medical Laboratory Technology

Moving sum of number of positive patient results as a quality control tool

Jiakai Liu et al.

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2017)

Article Medical Laboratory Technology

Average of delta: a new quality control tool for clinical laboratories

Graham R. D. Jones

ANNALS OF CLINICAL BIOCHEMISTRY (2016)

Article Medical Laboratory Technology

Clinically relevant lot-to-lot reagent difference in a commercial immunoturbidimetric assay for glycated hemoglobin A1c

Markus A. Thaler et al.

CLINICAL BIOCHEMISTRY (2015)

Article Medical Laboratory Technology

Failure of Current Laboratory Protocols to Detect Lot-to-Lot Reagent Differences: Findings and Possible Solutions

Alicia Algeciras-Schimnich et al.

CLINICAL CHEMISTRY (2013)

Letter Pathology

Clinical consequences of erroneous laboratory results that went unnoticed for 10 days

Tze Ping Loh et al.

JOURNAL OF CLINICAL PATHOLOGY (2013)

Article Medical Laboratory Technology

Commutability Limitations Influence Quality Control Results with Different Reagent Lots

W. Greg Miller et al.

CLINICAL CHEMISTRY (2011)

Article Medical Laboratory Technology

The exponentially weighted moving average (EWMA) rule compared with traditionally used quality control rules

K Linnet

CLINICAL CHEMISTRY AND LABORATORY MEDICINE (2006)