Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.
Clin Chem Lab Med
; 62(4): 635-645, 2024 Mar 25.
Article
en En
| MEDLINE
| ID: mdl-37982680
ABSTRACT
OBJECTIVES:
Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application.METHODS:
Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals' independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model.RESULTS:
Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using platelets as an example, it was found that for 20â¯% bias, both positive and negative, the uncertainty of error detection for mNL-PBRTQC was smallest at the median and maximum values.CONCLUSIONS:
mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Revista:
Clin Chem Lab Med
Asunto de la revista:
QUIMICA CLINICA
/
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2024
Tipo del documento:
Article