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Recent evolutions of machine learning applications in clinical laboratory medicine

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Abstract
Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.
Keywords
CONVOLUTIONAL NEURAL-NETWORKS, RAPID DETECTION, RAMAN-SPECTROSCOPY, CLASSIFICATION, BLOOD, IDENTIFICATION, RECOGNITION, ALGORITHMS, PREDICTION, Artificial intelligence, clinical applications, machine learning, medical laboratory science

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MLA
De Bruyne, Sander, et al. “Recent Evolutions of Machine Learning Applications in Clinical Laboratory Medicine.” CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES, vol. 58, no. 2, 2021, pp. 131–52, doi:10.1080/10408363.2020.1828811.
APA
De Bruyne, S., Speeckaert, M., Van Biesen, W., & Delanghe, J. (2021). Recent evolutions of machine learning applications in clinical laboratory medicine. CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES, 58(2), 131–152. https://doi.org/10.1080/10408363.2020.1828811
Chicago author-date
De Bruyne, Sander, Marijn Speeckaert, Wim Van Biesen, and Joris Delanghe. 2021. “Recent Evolutions of Machine Learning Applications in Clinical Laboratory Medicine.” CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES 58 (2): 131–52. https://doi.org/10.1080/10408363.2020.1828811.
Chicago author-date (all authors)
De Bruyne, Sander, Marijn Speeckaert, Wim Van Biesen, and Joris Delanghe. 2021. “Recent Evolutions of Machine Learning Applications in Clinical Laboratory Medicine.” CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES 58 (2): 131–152. doi:10.1080/10408363.2020.1828811.
Vancouver
1.
De Bruyne S, Speeckaert M, Van Biesen W, Delanghe J. Recent evolutions of machine learning applications in clinical laboratory medicine. CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES. 2021;58(2):131–52.
IEEE
[1]
S. De Bruyne, M. Speeckaert, W. Van Biesen, and J. Delanghe, “Recent evolutions of machine learning applications in clinical laboratory medicine,” CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES, vol. 58, no. 2, pp. 131–152, 2021.
@article{8691418,
  abstract     = {{Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.}},
  author       = {{De Bruyne, Sander and Speeckaert, Marijn and Van Biesen, Wim and Delanghe, Joris}},
  issn         = {{1040-8363}},
  journal      = {{CRITICAL REVIEWS IN CLINICAL LABORATORY SCIENCES}},
  keywords     = {{CONVOLUTIONAL NEURAL-NETWORKS,RAPID DETECTION,RAMAN-SPECTROSCOPY,CLASSIFICATION,BLOOD,IDENTIFICATION,RECOGNITION,ALGORITHMS,PREDICTION,Artificial intelligence,clinical applications,machine learning,medical laboratory science}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{131--152}},
  title        = {{Recent evolutions of machine learning applications in clinical laboratory medicine}},
  url          = {{http://dx.doi.org/10.1080/10408363.2020.1828811}},
  volume       = {{58}},
  year         = {{2021}},
}

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