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

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 u...

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Main Authors: Goh, Kim Huat, Wang, Le, Yeow, Adrian Yong Kwang, Poh, Hermione, Li, Ke, Yeow, Joannas Jie Lin, Tan, Gamaliel Yu Heng
Other Authors: Nanyang Business School
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146395
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1463952023-05-19T07:31:15Z Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare Goh, Kim Huat Wang, Le Yeow, Adrian Yong Kwang Poh, Hermione Li, Ke Yeow, Joannas Jie Lin Tan, Gamaliel Yu Heng Nanyang Business School Science Artificial Intelligence Healthcare 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. Ministry of Education (MOE) Published version This work is supported by the Ministry of Education, Singapore, under the Social Science Research Council Thematic Grant. Grant number: MOE2017-SSRTG-030. 2021-02-16T02:09:39Z 2021-02-16T02:09:39Z 2021 Journal Article Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021). Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature Communications, 12(1), 711-. doi:10.1038/s41467-021-20910-4 2041-1723 https://hdl.handle.net/10356/146395 10.1038/s41467-021-20910-4 33514699 2-s2.0-85100110345 1 12 711 en Nature Communications © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science
Artificial Intelligence
Healthcare
spellingShingle Science
Artificial Intelligence
Healthcare
Goh, Kim Huat
Wang, Le
Yeow, Adrian Yong Kwang
Poh, Hermione
Li, Ke
Yeow, Joannas Jie Lin
Tan, Gamaliel Yu Heng
Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
description 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.
author2 Nanyang Business School
author_facet Nanyang Business School
Goh, Kim Huat
Wang, Le
Yeow, Adrian Yong Kwang
Poh, Hermione
Li, Ke
Yeow, Joannas Jie Lin
Tan, Gamaliel Yu Heng
format Article
author Goh, Kim Huat
Wang, Le
Yeow, Adrian Yong Kwang
Poh, Hermione
Li, Ke
Yeow, Joannas Jie Lin
Tan, Gamaliel Yu Heng
author_sort Goh, Kim Huat
title Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
title_short Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
title_full Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
title_fullStr Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
title_full_unstemmed Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
title_sort artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
publishDate 2021
url https://hdl.handle.net/10356/146395
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