A New Paradigm For Predicting Past And Future Out of Control Events In Internal Quality Control: Gaussian Process For Machine Learning

Yıl 2022, Cilt: 2 Sayı: 3, 19 - 26, 31.12.2022

Banu Isbilen Basok , Ali Rıza Şişman

Öz

controlling
the reliability of a laboratory test before running patient samples.
Currently used IQC process focus on the management of Total Analytical
Error (TAE) using rule-based approaches. The process cannot predict timings
of Total Allowable Error (TEa) violations, precisely. In the study,
we proposed a predictive computational approach for IQC, Predictive
Quality Control Algorithm (PQCA), to solve with this problem using
Gaussian Process for Machine Learning (GPML) method. The software
implementation carried out in Python and Scikit-learn library running on a standard Windows-based PC. A digital control chart
based on PQCA was introduced. It is demonstrated that observations
fall within the 95% confidence intervals of their
corresponding predictions generated by PQCA. It also presented
that TAE calculated using classical formula is unable
to capture all violations of TEa. PQCA is a simple procedure
that can directly relate raw control data to quality targets and
enabled a predictive approach with a high degree of accuracy.
The classical TAE calculation model is based on a univariate
Gaussian model. GPML, which PQCA is based on,
is generalized by a multivariate Gaussian. Therefore, PQCA
can be viewed as a generalization of the classical IQC model.
Using PQCA, laboratories can take a proactive approach to
the control of analytical quality, meet regulatory institutions’
requirements, and hence provide better patient outcomes.
PQCA based IQC can achieve controlling of analytical variability
using a single algorithm overcoming the shortcomings
of conventional methods. In the future, newly available computational
models make possible more sophisticated, predictive
mathematical frameworks for IQC.

Anahtar Kelimeler

Quality assurance, internal quality control, Gaussian Process Machine Learning, predictive quality control algorithm, quality control procedure

İç Kalite Kontrol Süreçlerinde Geçmiş ve Gelecekteki Kontrol Dışı Olayları Tahmin Etmede Yeni Bir Paradigma: Makine Öğrenimi için Gaussian Modeli

Öz

İç Kalite Kontrol (İKK), hasta numunelerini çalıştırmadan
önce bir laboratuvar testinin güvenilirliğini değerlendirme ve
kontrol etme sürecidir. Mevcut İKK süreci, kural tabanlı yaklaşımlar
kullanarak Toplam Analitik Hatanın (TAE) yönetimine
odaklanmaktadır. Toplam İzin Verilebilir Hata (TEa)
ihlallerinin zamanlamasını tam olarak tahmin edemez. Çalışmada,
Tahmine Dayalı Kalite Kontrol Algoritması (PQCA)
için Gaussian Process for Machine Learning (GPML) yöntemini
kullanarak İKK sürecini değerlendirmede tahmine dayalı
bir hesaplama yaklaşımı önerildi. Python ve Scikit-learn
kütüphanesinde yürütülen yazılım uygulaması, Windows tabanlı
standart bir PC üzerinde çalıştırıldı. PQCA’ya dayalı bir
dijital kontrol tablosu oluşturuldu. Gözlemlerin, PQCA tarafından
üretilen karşılık gelen tahminlerinin %95 güven aralığı
içinde kaldığı gösterildi. Ayrıca, klasik formül kullanılarak
hesaplanan TAE’nin tüm TEa ihlallerini yakalayamadığı
da ortaya konuldu. PQCA, ham kontrol verilerini doğrudan
kalite hedefleriyle ilişkilendirebilen basit bir prosedür olup,
yüksek derecede doğrulukla tahmine dayalı bir yaklaşım
sağlamıştır. Klasik TAE hesaplama modeli, tek değişkenli
bir Gauss modeline dayanır. PQCA’nın temel aldığı GPML,
çok değişkenli bir Gaussian modeldir. Bu nedenle PQCA,
klasik IQC modelinin bir genellemesi olarak görülebilir. Laboratuvarlar,
PQCA’yı kullanarak analitik kalitenin kontrolüne
proaktif bir yaklaşım getirebilir, düzenleyici kurumların
gereksinimlerini karşılayabilir ve dolayısıyla daha doğru ve
güvenilir hasta sonuçları sağlayabilir. PQCA tabanlı İKK,
geleneksel yöntemlerin eksikliklerinin üstesinden gelen tek
bir algoritma kullanarak analitik değişkenliğin kontrolünü
sağlayabilir. Gelecekte, yeni kullanılabilir hesaplama modelleri,
İKK için daha karmaşık, tahmine dayalı matematiksel
çerçeveleri mümkün kılacaktır.

Anahtar Kelimeler

Kalite güvencesi, iç kalite kontrolü, Gauss Süreci Makine Öğrenimi, tahmine dayalı kalite kontrol algoritması, kalite kontrol prosedürü, toplam analitik hata

Kaynakça

• 1. Clinical and Laboratory Standards Institute (CLSI). Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions; Approved Guideline—Third Edition. CLSI document C24-A3 (ISBN 1-56238-613-1). 2006 Vol. 26 No. 25. Clinical and Laboratory Standards Institute, 950 West Valley Road, Suite 2500, Wayne, Pennsylvania 19087 USA, 2006. Clinical & Laboratory Standards Institute C24-A3. Statistical Quality Control for Quantitative Measurement Procedures: Principles and Definitions; Approved Guideline— Third Edition.
• 2. United States Department of Health and Human Services. Medicare, Medicaid, and CLIA programs: regulations implementing the Clinical Laboratory Improvement Amendments of 1988 (CLIA). Final rule. 57 Federal Register 7002-7186; 1992. Available at: www.phppo.cdc.gov/clia/regs/toc.aspx. Accessed February 11, 2020.
• 3. Westgard JO, Barry PL, Hunt MR, Groth T. A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 1981;27:493-501.
• 4. Westgard JO, Westgard SA. Measuring Analytical Quality: Total Analytical Error Versus Measurement Uncertainty. Clin Lab Med 2017;37 (1):1-13.
• 5. https://www.westgard.com/biodatabase1.htm. Accessed December 27, 2022.
• 6. Rasmussen CE, Williams CKI. Gaussian processes for machine learning, Cambridge (MA): the MIT Press, 2006, p. 13-21, ISBN 026218253X.
• 7. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. Journal of machine learning research 2011;12:2825-30.
• 8. Futoma J, Sendak M, Cameron CB, Heller K. Scalable modeling of multivariate longitudinal data for prediction of chronic kidney disease progression. Machine Learning and Healthcare Conference, Los Angeles, CA. 2016, eprint arXiv:1608.046152016arXiv160804615F
• 9. Colopy GW, Pimentel MAF, Roberts SJ, Clifton DA. Bayesian gaussian processes for identifying the deteriorating patient. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL. 2016, 5311-4 p. doi: 10.1109/EMBC.2016.7591926
• 10. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et. al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990-2015. JAMA. 2017;317(2):165–82. doi:10.1001/jama.2016.1904.
• 11. Rasmussen CE, Williams CKI. Gaussian processes for machine learning, Cambridge (MA): the MIT Press, 2006, p. 79-85, ISBN 026218253X.
• 12. Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova. Artificial intelligence, bias and clinical safety. BMJ Qual Saf 2019;28:231–237.
• 13. 510(K) Summary: DiffMaster Octavia. https://www.accessdata.fda.gov/cdrh_docs/pdf/K003301.pdf. Accessed February 11, 2020.
• 14. Pancholi P, Carroll KC, Buchan BW et al. Multicenter evaluation of the accelerate phenotest BC kit for rapid identification and phenotypic antimicrobial susceptibility testing using morphokinetic cellular analysis. J Clin Microbiol 2018;56:4:1-21.
• 15. Cao Y, Cheng M, Hu C. UrineCART, a machine learning method for establishment of review rules based on UF-1000i flow cytometry and dipstick or reflectance photometer. Clin Chem Lab Med 2012;50:2155-61.
• 16. Durant TJS, Olson EM, Schulz WL et al. Very deep convolutional neural networks for morphologic classification of erythrocytes. Clin Chem 2017;63:1847-55.
• 17. Wilkes EH, Rummsby G, Woodward GM. Using machine learning to aid the interpretation of urine steroid profiles. Clin Chem 2018;64(11):1586-95.
• 18. Demirci F, Akan P, Kume T et al. Artifical neural network approach in laboratory test reporting: learning algorithms. Am J Clin Pathol 2016;146:227-37.
• 19. Luo Y, Szolovits P, Anand S. Dighe AS, Baron JM. Using Machine Learning to Predict Laboratory Test Results. Am J Clin Pathol 2016;145:778-88.
• 20. Guncar G, Kukar M, Notar M, Brvar M, et al. An application of machine learning to haematological diagnosis. Sci Rep 2018;8:411.
• 21. Yardim, M. Total analytical error assessment of Yerköy State Hospital biochemistry laboratory. International Journal of Medical Biochemistry 2022, 5(1),