• Critical care clinics · Oct 2024

    Review

    A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models.

    • João Matos, Jack Gallifant, Anand Chowdhury, Nicoleta Economou-Zavlanos, Marie-Laure Charpignon, Judy Gichoya, Leo Anthony Celi, Lama Nazer, Heather King, and WongAn-Kwok IanAIDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Division of Translational Biomedical Informatics, Durha.
    • University of Porto (FEUP), Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
    • Crit Care Clin. 2024 Oct 1; 40 (4): 827857827-857.

    AbstractThis narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.Copyright © 2024 Elsevier Inc. All rights reserved.

      Pubmed     Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…

What will the 'Medical Journal of You' look like?

Start your free 21 day trial now.

We guarantee your privacy. Your email address will not be shared.