Detecting anomalous sequences in electronic health records using higher-order tensor networks-Reference-Cited by-同舟云学术

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Published:2022-11 Issue: Volume:135 Page:104219
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Niu Haoran^ORCID,Omitaomu Olufemi A.^ORCID,Langston Michael A.^ORCID,Olama Mohammad,Ozmen Ozgur^ORCID,Klasky Hilda B.,Laurio Angela^ORCID,Sauer Brian,Ward Merry,Nebeker Jonathan^ORCID

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference51 articles.

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4. A clustering approach for detecting implausible observation values in electronic health records data;Estiri;BMC Med. Inform. Decis. Making,2019

5. Sequence aggregation rules for anomaly detection in computer network traffic;Radford,2018

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