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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

112 Scopus Citations
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Author(s)

  • Kim Huat Goh
  • Adrian Yong Kwang Yeow
  • Hermione Poh
  • Ke Li
  • Joannas Jie Lin Yeow
  • Gamaliel Yu Heng Tan

Detail(s)

Original languageEnglish
Article number711
Journal / PublicationNature Communications
Volume12
Online published29 Jan 2021
Publication statusPublished - 2021
Externally publishedYes

Link(s)

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.

Research Area(s)

Citation Format(s)

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. / Goh, Kim Huat; Wang, Le; Yeow, Adrian Yong Kwang et al.
In: Nature Communications, Vol. 12, 711, 2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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