Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
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Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. ⋯ We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.
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Clin. Microbiol. Infect. · Oct 2020
ReviewMachine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies.
Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. ⋯ Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.
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Clin. Microbiol. Infect. · Oct 2020
Observational StudyBacterial and fungal coinfection among hospitalized patients with COVID-19: a retrospective cohort study in a UK secondary-care setting.
To investigate the incidence of bacterial and fungal coinfection of hospitalized patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this retrospective observational study across two London hospitals during the first UK wave of coronavirus disease 2019 (COVID-19). ⋯ We found a low frequency of bacterial coinfection in early COVID-19 hospital presentation, and no evidence of concomitant fungal infection, at least in the early phase of COVID-19.
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Clin. Microbiol. Infect. · Oct 2020
Predictors of progression from moderate to severe coronavirus disease 2019: a retrospective cohort.
Most cases of coronavirus disease 2019 (COVID-19) are identified as moderate, which is defined as having a fever or dry cough and lung imaging with ground-glass opacities. The risk factors and predictors of prognosis in such cohorts remain uncertain. ⋯ Higher levels of NLR and CRP at admission were associated with poor prognosis of individuals with moderate COVID-19. NLR and CRP were good predictors of progression to critical condition and death.