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Machine Learning-based Sample Misidentification Error Detection In Clinical Laboratory Tests: A Retrospective Multicenter Study.

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Machine Learning-based Sample Misidentification Error Detection In Clinical Laboratory Tests: A Retrospective Multicenter Study.

Related Experiment Video

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

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Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective

Hyeon Seok Seok^1,2, Shinae Yu³, Kyung-Hwa Shin⁴

¹Interdisciplinary Program of Biomedical Engineering, Graduate School, Chonnam National University, Yeosu, Republic of Korea.

Clinical Chemistry

|August 22, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning models significantly improve tumor marker test error detection, outperforming conventional methods in accuracy and sensitivity. This enhances diagnostic reliability and laboratory efficiency, especially for smaller facilities.

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Automatic Separation and Collection of Cancer-Related Substances from Clinical Samples

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Area of Science:

Clinical diagnostics
Laboratory automation
Machine learning in healthcare

Background:

Autoverification technologies are vital for diagnostic accuracy in clinical labs.
Conventional methods lack sensitivity and efficiency for error detection.
Tumor marker testing requires robust error detection to ensure reliable results.

Purpose of the Study:

To introduce and evaluate a machine learning (ML)-based autoverification technology for enhanced tumor marker test error detection.
To compare the performance of ML models against conventional delta check methods.

Main Methods:

Trained and validated ML models on a large dataset (397,751 samples for training, 215,339 for external validation).
Simulated sample misidentification errors at a 1% rate.

Optimized ML models using Bayesian optimization and validated across multiple institutions.

Main Results:

Deep neural networks and extreme gradient boosting achieved superior ROC AUC (0.834-0.903) compared to conventional methods (0.705-0.816).
ML models demonstrated higher balanced accuracy (0.760-0.836) in external validation than conventional models (0.670-0.773).

Conclusions:

ML-based autoverification significantly improves detection of sample misidentification errors.
These models offer a versatile solution to enhance efficiency and reliability in clinical laboratories, including smaller ones.
The study presents a pathway towards more dependable clinical laboratory testing.