Early Nephrosis Detection Based on Deep Learning with Clinical Time-Series Data.

Decision support techniques nephrosis supervised machine learning

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
21 Aug 2019
Historique:
entrez: 24 8 2019
pubmed: 24 8 2019
medline: 13 9 2019
Statut: ppublish

Résumé

Nephrosis is disease characterized by abnormal protein loss from impaired kidney. We constructed early prediction model using machine learning from clinical time series data, that can predict onset of nephrosis for more than one month. Long short-term memory capable of recognizing temporal sequential data patterns, was adopted as early prediction model for nephrosis. We verified our proposed prediction model has higher accuracy compared with those of baseline classifiers by 5-fold cross validation.

Identifiants

pubmed: 31438249
pii: SHTI190552
doi: 10.3233/SHTI190552
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1596-1597

Auteurs

Yohei Yamasaki (Y)

Graduate School of Informatics Kyoto University, Kyoto-City, Kyoto, Japan.

Osamu Sugiyama (O)

Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Shusuke Hiragi (S)

Graduate School of Informatics Kyoto University, Kyoto-City, Kyoto, Japan.
Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Shosuke Ohtera (S)

Graduate School of Informatics Kyoto University, Kyoto-City, Kyoto, Japan.
Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Goshiro Yamamoto (G)

Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Hiroshi Sasaki (H)

Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Kazuya Okamoto (K)

Graduate School of Informatics Kyoto University, Kyoto-City, Kyoto, Japan.
Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Masayuki Nambu (M)

Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

Tomohiro Kuroda (T)

Graduate School of Informatics Kyoto University, Kyoto-City, Kyoto, Japan.
Kyoto University Hospital, Kyoto-City, Kyoto, Japan.

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