Name
Title | ||
|---|---|---|
The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. |
Abstract | ||
|---|---|---|
Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ijmedinf.2020.104176 | International Journal of Medical Informatics |
Keywords | DocType | Volume |
Artificial intelligence,AI,Sepsis,XGBoost,ICU,Critical care,Diagnostic algorithm | Journal | 141 |
ISSN | Citations | PageRank |
1386-5056 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuo-Ching Yuan | 1 | 0 | 0.34 |
| Lung-Wen Tsai | 2 | 0 | 0.34 |
| Ko-Han Lee | 3 | 0 | 0.34 |
| Yi-Wei Cheng | 4 | 0 | 0.34 |
| Shou-Chieh Hsu | 5 | 0 | 0.34 |
| Yu-Sheng Lo | 6 | 93 | 3.49 |
| Ray-Jade Chen | 7 | 37 | 3.21 |