Name
Abstract | ||
|---|---|---|
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone calculations that are further hindered by the use of static variable thresholds derived from aggregate patient populations. These coarse frameworks do not capture time-sensitive individual physiological patterns and are not suitable for instantaneous assessment of patientsu0027 acuity trajectories, a critical task for the ICU where conditions often change rapidly. Furthermore, they are ill-suited to capitalize on the emerging availability of streaming electronic health record data. We propose a novel acuity score framework (DeepSOFA) that leverages temporal patient measurements in conjunction with deep learning models to make accurate assessments of a patientu0027s illness severity at any point during their ICU stay. We compare DeepSOFA with SOFA baseline models using the same predictors and find that at any point during an ICU admission, DeepSOFA yields more accurate predictions of in-hospital mortality. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Artificial intelligence,Medical record,Static variable,Physical medicine and rehabilitation,Deep learning,Medicine,Illness severity |
DocType | Volume | Citations |
Journal | abs/1802.10238 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Shickel | 1 | 48 | 2.84 |
| Tyler J. Loftus | 2 | 0 | 0.34 |
| Tezcan Ozrazgat-Baslanti | 3 | 0 | 0.34 |
| Ashkan Ebadi | 4 | 48 | 6.90 |
| Azra Bihorac | 5 | 50 | 8.63 |
| Parisa Rashidi | 6 | 0 | 0.34 |