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
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