Recent evolutions of machine learning applications in clinical laboratory medicine.
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-ba...
| Publicado en: | Critical Reviews in Clinical Laboratory Sciences Vol. 58; no. 2; pp. 131 - 153 |
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| Autores principales: | , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149105163&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149105163 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10408363 1AV jtl: Critical Reviews in Clinical Laboratory Sciences issn: 10408363 maglogo: Y pubinfo: dt: Mar2021 vid: 58 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 149105163 146386476 149105163 149105163 10.1080/10408363.2020.1828811 149105163 ppf: 131 ppct: 22 formats: fmt: @attributes: type: P tig: atl: Recent evolutions of machine learning applications in clinical laboratory medicine. aug: au: De Bruyne, Sander Speeckaert, Marijn M. Van Biesen, Wim Delanghe, Joris R. affil: Department of Diagnostic Sciences, Ghent University, Ghent, Belgium sug: subj: Machine Learning Utilization Clinical Laboratories Trends Medicine Workflow Algorithms Technology, Medical Artificial Intelligence Automation, Laboratory Quality Assessment Urinalysis Biochemistry Sensitivity and Specificity Hematology Microbiology Workload Chlamydiales Analysis Drug Resistance, Microbial Evaluation Diagnosis, Laboratory ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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