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
. 2025 Nov 4;4(11):e0001045.
doi: 10.1371/journal.pdig.0001045. eCollection 2025 Nov.

Pediatric sepsis prediction: Human in the loop framework

Affiliations

Pediatric sepsis prediction: Human in the loop framework

Radha Nagarajan et al. PLOS Digit Health. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Critical stages in the pediatric sepsis continuum (I, Initiation; S, Sepsis; SV, Severe Sepsis; SH, Septic Shock) and adverse outcomes is shown by (1) and (2), respectively.
Current pediatric sepsis alert generation using structured data elements (3) is represented by the black box (Current), and the proposed augmented human-in-the-loop (HITL) framework using multimodal data and concerted working of providers with structured (3) and unstructured data elements (4), (5), (6) is shown by the blue box.

References

    1. Watson RS, Carrol ED, Carter MJ, Kissoon N, Ranjit S, Schlapbach LJ. The burden and contemporary epidemiology of sepsis in children. Lancet Child Adolesc Health. 2024;8(9):670–81. doi: 10.1016/S2352-4642(24)00140-8 - DOI - PubMed
    1. Emr BM, Alcamo AM, Carcillo JA, Aneja RK, Mollen KP. Pediatric sepsis update: how are children different?. Surg Infect (Larchmt). 2018;19(2):176–83. doi: 10.1089/sur.2017.316 - DOI - PubMed
    1. Tennant R, Graham J, Kern J, Mercer K, Ansermino JM, Burns CM. A scoping review on pediatric sepsis prediction technologies in healthcare. NPJ Digit Med. 2024;7(1):353. doi: 10.1038/s41746-024-01361-9 - DOI - PMC - PubMed
    1. Rangan ES, Pathinarupothi RK, Anand KJS, Snyder MP. Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning. JAMIA Open. 2022;5(4):ooac080. doi: 10.1093/jamiaopen/ooac080 - DOI - PMC - PubMed
    1. Sepanski RJ, Godambe SA, Mangum CD, Bovat CS, Zaritsky AL, Shah SH. Designing a pediatric severe sepsis screening tool. Front Pediatr. 2014;2:56. doi: 10.3389/fped.2014.00056 - DOI - PMC - PubMed

LinkOut - more resources