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

Author

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  • 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)

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.

Suggested Citation

  • Kim Huat Goh & Le Wang & Adrian Yong Kwang Yeow & Hermione Poh & Ke Li & Joannas Jie Lin Yeow & Gamaliel Yu Heng Tan, 2021. "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-20910-4
    DOI: 10.1038/s41467-021-20910-4
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