Name
Abstract | ||
|---|---|---|
•Semi-supervised models were used to overcome generalizability of prediction models.•External validation was performed to rigorously compare the model performance.•Semi-supervised models outperformed supervised learning models in risk prediction. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jbi.2019.103117 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Generalizability,Clinical usefulness,Colorectal cancer (CRC),External validation,Prediction model,Semi-supervised learning (SSL) | Generalizability theory,Interpretability,Data mining,Semi-supervised learning,Computer science,Epidemiology,Supervised learning,Predictive modelling,Logistic regression,Cohort | Journal |
Volume | ISSN | Citations |
92 | 1532-0464 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengqiang Chi | 1 | 1 | 1.05 |
| Xinhang Li | 2 | 0 | 0.34 |
| Yu Tian | 3 | 3 | 3.11 |
| Jun Li | 4 | 0 | 0.34 |
| Xiangxing Kong | 5 | 0 | 0.34 |
| Ke-Feng Ding | 6 | 3 | 3.43 |
| Chunhua Weng | 7 | 547 | 75.69 |
| Jing-Song Li | 8 | 222 | 11.02 |