Name
Title | ||
|---|---|---|
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics. |
Abstract | ||
|---|---|---|
Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data. |
Year | Venue | Field |
|---|---|---|
2018 | PloS one | Generalizability theory,Framingham Risk Score,Data mining,Acute kidney injury,Receiver operating characteristic,Emergency medicine,Computer science,Perioperative,Blood pressure,Predictive modelling,Cohort |
DocType | Volume | Citations |
Journal | abs/1805.05452 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lasith Adhikari | 1 | 0 | 0.34 |
| Tezcan Ozrazgat-Baslanti | 2 | 1 | 2.37 |
| Paul Thottakkara | 3 | 0 | 0.34 |
| Ashkan Ebadi | 4 | 48 | 6.90 |
| Amir Motaei | 5 | 0 | 0.34 |
| Parisa Rashidi | 6 | 859 | 46.92 |
| Xiaolin Li | 7 | 243 | 17.57 |
| Azra Bihorac | 8 | 50 | 8.63 |