Risk Prediction of Diabetic Nephropathy via Interpretable Feature Extraction from EHR Using Convolutional Autoencoder.

Katsuki, Takayuki; Ono, Masaki; Koseki, Akira; Kudo, Michiharu; Haida, Kyoichi; Kuroda, Jun; Makino, Masaki; Yanagiya, Ryosuke; Suzuki, Atsushi

Katsuki, Takayuki; Ono, Masaki; Koseki, Akira; Kudo, Michiharu; Haida, Kyoichi; Kuroda, Jun; Makino, Masaki; Yanagiya, Ryosuke; Suzuki, Atsushi.

Afiliación

Katsuki T; IBM Research - Tokyo, Japan.
Ono M; IBM Research - Tokyo, Japan.
Koseki A; IBM Research - Tokyo, Japan.
Kudo M; IBM Research - Tokyo, Japan.
Haida K; Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Japan.
Kuroda J; IT Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Japan.
Makino M; Division of Endocrinology and Metabolism, Department of Internal Medicine, Fujita Health University, Japan.
Yanagiya R; Division of Medical Information Systems, Fujita Health University, Japan.
Suzuki A; Division of Endocrinology and Metabolism, Department of Internal Medicine, Fujita Health University, Japan.

Stud Health Technol Inform ; 247: 106-110, 2018.

Article en En | MEDLINE | ID: mdl-29677932

Asunto(s)

Nefropatías Diabéticas; Registros Electrónicos de Salud; Minería de Datos; Humanos; Proyectos de Investigación; Riesgo

Palabras clave

Convolutional Autoencoder; Diabetic Nephropathy; Electronic Health Record; Feature Extraction; Kidney Disease; Risk Prediction

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Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Nefropatías Diabéticas / Registros Electrónicos de Salud Tipo de estudio: Etiology_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2018 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Países Bajos

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