Name
Title | ||
|---|---|---|
A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data |
Abstract | ||
|---|---|---|
•Information varying during HD sessions was captured for mortality prediction.•Anomaly detection solved the data-imbalance problem caused by follow-up loss.•Features contributed the most may provide hints for HD clinical intervention. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jbi.2021.103930 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Hemodialysis,Mortality prediction,LSTM autoencoders,Anomaly detection,Electronic health records | Journal | 123 |
ISSN | Citations | PageRank |
1532-0464 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Wang | 1 | 167 | 15.47 |
| Yilin Zhu | 2 | 0 | 0.68 |
| Guofeng Lou | 3 | 0 | 0.34 |
| Ping Zhang | 4 | 0 | 1.01 |
| Jianghua Chen | 5 | 1 | 2.05 |
| Jing-Song Li | 6 | 222 | 11.02 |