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

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

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

Afiliação

Niu H; University of Tennessee, Knoxville, TN, 37996, United States.
Omitaomu OA; University of Tennessee, Knoxville, TN, 37996, United States; Oak Ridge National Laboratory, Oak Ridge, TN, 37831, United States. Electronic address: omitaomuoa@ornl.gov.
Langston MA; University of Tennessee, Knoxville, TN, 37996, United States.
Olama M; Oak Ridge National Laboratory, Oak Ridge, TN, 37831, United States.
Ozmen O; Oak Ridge National Laboratory, Oak Ridge, TN, 37831, United States.
Klasky HB; Oak Ridge National Laboratory, Oak Ridge, TN, 37831, United States.
Laurio A; Department of Veterans Affairs, Washington DC, 20420, United States.
Sauer B; Department of Veterans Affairs, Washington DC, 20420, United States.
Ward M; Department of Veterans Affairs, Washington DC, 20420, United States.
Nebeker J; Department of Veterans Affairs, Washington DC, 20420, United States.

J Biomed Inform ; 135: 104219, 2022 11.

Article em En | MEDLINE | ID: mdl-36243337

Assuntos

Algoritmos; Registros Eletrônicos de Saúde; Humanos

Palavras-chave

Anomaly detection; Electronic health records; Event sequence; Higher-order tensor network

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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Assunto principal: Algoritmos / Registros Eletrônicos de Saúde Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Biomed Inform Ano de publicação: 2022 Tipo de documento: Article

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