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  1. Article ; Online: Reply to "COVID-19 prediction models should adhere to methodological and reporting standards".

    Wu, Guangyao / Woodruff, Henry C / Chatterjee, Avishek / Lambin, Philippe

    The European respiratory journal

    2020  Volume 56, Issue 3

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Humans ; Models, Theoretical ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-09-10
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 639359-7
    ISSN 1399-3003 ; 0903-1936
    ISSN (online) 1399-3003
    ISSN 0903-1936
    DOI 10.1183/13993003.02918-2020
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2322: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 292: Show issues

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  2. Article ; Online: COVID-19 prediction models should adhere to methodological and reporting standards.

    Collins, Gary S / van Smeden, Maarten / Riley, Richard D

    The European respiratory journal

    2020  Volume 56, Issue 3

    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Decision Support Systems, Clinical ; Humans ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2 ; Triage
    Keywords covid19
    Language English
    Publishing date 2020-09-10
    Publishing country England
    Document type Letter ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 639359-7
    ISSN 1399-3003 ; 0903-1936
    ISSN (online) 1399-3003
    ISSN 0903-1936
    DOI 10.1183/13993003.02643-2020
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2322: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 292: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article ; Online: COVID-19 prediction models should adhere to methodological and reporting standards.

    Collins, GS / van Smeden, M / Riley, RD

    2020  

    Keywords Q Science (General) ; R Medicine (General) ; covid19
    Language English
    Publishing date 2020-07-23
    Publisher European Respiratory Society: ERJ
    Publishing country uk
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  6. Article ; Online: Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

    Chee, Marcel Lucas / Ong, Marcus Eng Hock / Siddiqui, Fahad Javaid / Zhang, Zhongheng / Lim, Shir Lynn / Ho, Andrew Fu Wah / Liu, Nan

    medRxiv

    Abstract: ... in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise ... the current evidence on AI applications for COVID-19 in intensive care and emergency settings, focusing ... on methods, reporting standards, and clinical utility. Methods: We systematically searched PubMed, Embase ...

    Abstract Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings, focusing on methods, reporting standards, and clinical utility. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency or prehospital settings. We assessed predictive modelling studies using PROBAST (prediction model risk of bias assessment tool) and a modified TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement for AI. We critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Studies had low adherence to reporting guidelines, with particularly poor reporting on model calibration and blinding of outcome and predictor assessment. Of the remaining three studies, two evaluated the prognostic utility of deep learning-based lung segmentation software and one studied an AI-based system for resource optimisation in the ICU. These studies had similar issues in methodology, validation, and reporting. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.
    Keywords covid19
    Language English
    Publishing date 2021-02-18
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.02.15.21251727
    Database COVID19

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