« Previous AbstractFe-based metal organic framework derivative with enhanced Lewis acidity and hierarchical pores for excellent adsorption of oxygenated volatile organic compounds    Next AbstractDo caterpillars secrete 'oral secretions'? »

Clin Microbiol Infect

Title:		Machine learning in the clinical microbiology laboratory: has the time come for routine practice?
Author(s):		Peiffer-Smadja N; Delliere S; Rodriguez C; Birgand G; Lescure FX; Fourati S; Ruppe E;
Address:		"National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; Universite de Paris, IAME, INSERM, F-75018 Paris, France. Universite de Paris, Laboratoire de Parasitologie-Mycologie, Groupe Hospitalier Saint-Louis-Lariboisiere-Fernand-Widal, Assistance Publique-Hopitaux de Paris (AP-HP), Paris, France. Department of Prevention, Diagnosis and Treatment of Infections, Henri-Mondor Hospital, APHP, Universite Paris-Est Creteil, IMRB, INSERM U955, Creteil, France. National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK. Universite de Paris, IAME, INSERM, F-75018 Paris, France. Universite de Paris, IAME, INSERM, F-75018 Paris, France. Electronic address: etienne.ruppe@inserm.fr"
Journal Title:		Clin Microbiol Infect
Year:		2020
Volume:		20200212
Issue:		10
Page Number:		1300 - 1309
DOI:		10.1016/j.cmi.2020.02.006
ISSN/ISBN:		1469-0691 (Electronic) 1198-743X (Linking)
Abstract:		"BACKGROUND: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. AIMS: This narrative review aims to explore the current use of ML In clinical microbiology. SOURCES: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. CONTENT: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. IMPLICATIONS: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings"
Keywords:		Bacterial Infections/diagnosis/therapy *Clinical Laboratory Services *Data Analysis Humans *Information Technology *Machine Learning Microbial Sensitivity Tests Mycoses/diagnosis/therapy Parasitic Diseases/diagnosis/therapy Virus Diseases/diagnosis/therap;
Notes:		"MedlinePeiffer-Smadja, N Delliere, S Rodriguez, C Birgand, G Lescure, F-X Fourati, S Ruppe, E eng Review England 2020/02/18 Clin Microbiol Infect. 2020 Oct; 26(10):1300-1309. doi: 10.1016/j.cmi.2020.02.006. Epub 2020 Feb 12"