Name
Title | ||
|---|---|---|
A machine learning-based framework to identify type 2 diabetes through electronic health records. |
Abstract | ||
|---|---|---|
•A machine learning-based framework to identify type 2 diabetes subjects.•The framework achieved high identification performances (∼0.98 in average AUC).•The framework focused on reducing missing rate to identify more type 2 diabetes subjects. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ijmedinf.2016.09.014 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Electronic health records,Type 2 diabetes,Data mining,Feature engineering,Machine learning | Decision tree,False positive rate,Data mining,Naive Bayes classifier,Computer science,Support vector machine,Feature engineering,Artificial intelligence,Random forest,Logistic regression,Cohort,Machine learning | Journal |
Volume | ISSN | Citations |
97 | 1386-5056 | 20 |
PageRank | References | Authors |
1.44 | 11 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zheng | 1 | 20 | 1.78 |
| Wei Xie | 2 | 108 | 7.30 |
| Liling Xu | 3 | 20 | 1.44 |
| Xiaoying He | 4 | 20 | 1.44 |
| Ya Zhang | 5 | 1340 | 91.72 |
| Mingrong You | 6 | 20 | 1.44 |
| Gong Yang | 7 | 20 | 1.44 |
| You Chen | 8 | 116 | 10.74 |