Name
Title | ||
|---|---|---|
Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks. |
Abstract | ||
|---|---|---|
Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data extracted from Electronic Medical Records (EMR) of about 12000 patients who were admitted to the PICU over a period of more than 10 years were leveraged. The RNN model ingests a sequence of measurements which include physiologic observations, laboratory results, administered drugs and interventions, and generates temporally dynamic predictions for in-ICU mortality at user-specified times. The RNNu0027s ICU mortality predictions offer significant improvements over those from two clinically-used scores and static machine learning algorithms. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Machine Learning | Psychological intervention,Recurrent neural network,Pediatric intensive care unit,Medical record,Artificial intelligence,Trajectory,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1701.06675 | 5 |
PageRank | References | Authors |
0.51 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Aczon | 1 | 5 | 0.51 |
| D. Ledbetter | 2 | 5 | 0.51 |
| L. Ho | 3 | 5 | 0.51 |
| A. Gunny | 4 | 5 | 0.51 |
| A. Flynn | 5 | 5 | 0.51 |
| J. Williams | 6 | 6 | 1.24 |
| Randall C. Wetzel | 7 | 182 | 11.24 |