Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2019 Apr 24;23(1):136.
doi: 10.1186/s13054-019-2424-7.

Intelligently learning from data

Collaborators, Affiliations
Editorial

Intelligently learning from data

Edward Palmer et al. Crit Care. .
No abstract available

Keywords: Artificial intelligence; Machine learning; Statistical models.

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

    1. The future of healthcare: our vision for digital, data and technology in health and care. Policy paper. https://www.gov.uk/government/publications/the-future-of-healthcare-our-.... Accessed 11 Mar 2019
    1. Bordacconi Mats Joe, Larsen Martin Vinæs. Regression to causality: Regression-style presentation influences causal attribution. Research & Politics. 2014;1(2):205316801454809. doi: 10.1177/2053168014548092. - DOI
    1. Harrell F. Road map for choosing between statistical modeling and machine learning. http://www.fharrell.com/post/stat-ml/.. Accessed 20 Mar 2019
    1. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35. doi: 10.1186/s41747-018-0061-6. - DOI - PMC - PubMed
    1. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547–553. doi: 10.1097/CCM.0000000000002936. - DOI - PMC - PubMed

Publication types

LinkOut - more resources