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Comment
. 2023 Oct;24(10):869-871.
doi: 10.1097/pcc.0000000000003314. Epub 2023 Oct 3.

Unmasking Critical Illness: Using Machine Learning and Biomarkers to See What Lies Beneath

Daniel Balcarcel  1 , Julie C Fitzgerald  1   2   3 , Alicia M Alcamo  1   2   3
Affiliations
Comment

Unmasking Critical Illness: Using Machine Learning and Biomarkers to See What Lies Beneath

Daniel Balcarcel et al. Pediatr Crit Care Med. 2023 Oct.
No abstract available

Keywords: Biomarkers; Machine Learning; Phenotypes; Precision Medicine; Subtyping.

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to disclose. Copyright Form Disclosure: Drs. Fitzgerald and Alcamo’s institutions received funding from the National Institutes of Health (NIH). Drs. Fitzgerald and Alcamo received support for article research from the NIH. Dr. Balcarcel has disclosed that he does not have any potential conflicts of interest.

Figures

Figure 1:
Figure 1:
Previously, critical illness consisted of defining patients according to heterogenous clinical syndromes with significant, but ill-defined, overlap. Moving forward, critical care will rely on a combination of biomarkers, machine learning, and clinical data to identify more precise critical illness subtypes with important prognostic and therapeutic implications. Created with BioRender.com.
See this image and copyright information in PMC

Comment on

References

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