Early Nephrosis Detection Based on Deep Learning with Clinical Time-Series Data
Year: 2019
Type: book-chapter
Abstract: 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.
DOI: 10.3233/shti190552
Authors: Yamasaki Yohei, Sugiyama Osamu, Hiragi Shusuke, Ohtera Shosuke, Yamamoto Goshiro, Sasaki Hiroshi, Okamoto Kazuya, Nambu Masayuki, Kuroda Tomohiro
Institutions: Kyoto University, Kyoto University Hospital
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2990215944