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

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. Here, the authors develop an artificial intelligence algorithm which uses both structured data and unstructured clinical...

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Main Authors:	Kim Huat Goh, Le Wang, Adrian Yong Kwang Yeow, Hermione Poh, Ke Li, Joannas Jie Lin Yeow, Gamaliel Yu Heng Tan
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-01-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-20910-4

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spelling	doaj-f98ccc25d03c4520b342766bf3d9b25c2021-01-31T12:19:11ZengNature Publishing GroupNature Communications2041-17232021-01-0112111010.1038/s41467-021-20910-4Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcareKim Huat Goh0Le Wang1Adrian Yong Kwang Yeow2Hermione Poh3Ke Li4Joannas Jie Lin Yeow5Gamaliel Yu Heng Tan6Nanyang Business School, Nanyang Technological UniversityNanyang Business School, Nanyang Technological UniversitySchool of Business, Singapore University of Social SciencesGroup Medical Informatics Office, National University Health SystemGroup Medical Informatics Office, National University Health SystemGroup Medical Informatics Office, National University Health SystemGroup Medical Informatics Office, National University Health SystemEarly 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. Here, the authors develop an artificial intelligence algorithm which uses both structured data and unstructured clinical notes to predict sepsis.https://doi.org/10.1038/s41467-021-20910-4
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Kim Huat Goh Le Wang Adrian Yong Kwang Yeow Hermione Poh Ke Li Joannas Jie Lin Yeow Gamaliel Yu Heng Tan
spellingShingle	Kim Huat Goh Le Wang Adrian Yong Kwang Yeow Hermione Poh Ke Li Joannas Jie Lin Yeow Gamaliel Yu Heng Tan Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare Nature Communications
author_facet	Kim Huat Goh Le Wang Adrian Yong Kwang Yeow Hermione Poh Ke Li Joannas Jie Lin Yeow Gamaliel Yu Heng Tan
author_sort	Kim Huat Goh
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
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-01-01
description	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. Here, the authors develop an artificial intelligence algorithm which uses both structured data and unstructured clinical notes to predict sepsis.
url	https://doi.org/10.1038/s41467-021-20910-4
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Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

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