DRCNN<sub>TLe</sub>: A deep recurrent convolutional neural network with transfer learning through pre-trained embeddings for automated ICD coding.

Raz Bhutto, Sajida; Wu, Yifan; Zeng, Min; Wahab Dogar, Abdul; Ullah, Kaleem; Li, Min

DRCNN_TLe: A deep recurrent convolutional neural network with transfer learning through pre-trained embeddings for automated ICD coding.

Raz Bhutto, Sajida; Wu, Yifan; Zeng, Min; Wahab Dogar, Abdul; Ullah, Kaleem; Li, Min.

Afiliação

Raz Bhutto S; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Wu Y; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Zeng M; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: zengmin@csu.edu.cn.
Wahab Dogar A; Department of Liver Transplant, Pir Abdul Qadir Shah Jeelani Institute of Medical Sciences, Gambat, Sindh, Pakistan.
Ullah K; Department of Liver Transplant, Pir Abdul Qadir Shah Jeelani Institute of Medical Sciences, Gambat, Sindh, Pakistan.
Li M; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: limin@mail.csu.edu.cn.

Methods ; 205: 97-105, 2022 09.

Article em En | MEDLINE | ID: mdl-35781051

Assuntos

Codificação Clínica; Registros Eletrônicos de Saúde; Classificação Internacional de Doenças; Aprendizado de Máquina; Redes Neurais de Computação

Palavras-chave

Clinical notes; ICD coding; Liver disease; Pre-trained Embeddings; Recurrent Convolutional Neural Network; Transfer Learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Classificação Internacional de Doenças / Registros Eletrônicos de Saúde / Codificação Clínica Tipo de estudo: Prognostic_studies Idioma: En Revista: Methods Assunto da revista: BIOQUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos

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