Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Kim Huat Goh (), Le Wang (), Adrian Yong Kwang Yeow, Hermione Poh, Ke Li, Joannas Jie Lin Yeow and Gamaliel Yu Heng Tan
Additional contact information
Kim Huat Goh: Nanyang Business School, Nanyang Technological University
Le Wang: Nanyang Business School, Nanyang Technological University
Adrian Yong Kwang Yeow: School of Business, Singapore University of Social Sciences
Hermione Poh: Group Medical Informatics Office, National University Health System
Ke Li: Group Medical Informatics Office, National University Health System
Joannas Jie Lin Yeow: Group Medical Informatics Office, National University Health System
Gamaliel Yu Heng Tan: Group Medical Informatics Office, National University Health System

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-20910-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-20910-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-20910-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().