Name
Abstract | ||
|---|---|---|
Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9175575 | 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
Keywords | DocType | Volume |
Algorithms,Deep Learning,Emergency Medical Services,Hospitals,Humans,Neural Networks, Computer | Conference | 2020 |
ISSN | ISBN | Citations |
2375-7477 | 978-1-7281-1991-5 | 0 |
PageRank | References | Authors |
0.34 | 7 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingfeng Li | 1 | 0 | 0.34 |
| Richard Paris | 2 | 14 | 2.58 |
| Conner Pinson | 3 | 0 | 0.34 |
| Yan Wang | 4 | 0 | 0.34 |
| Joseph Coco | 5 | 2 | 1.43 |
| Jamison Heard | 6 | 0 | 0.34 |
| Julie A. Adams | 7 | 392 | 53.75 |
| Daniel V Fabbri | 8 | 0 | 0.34 |
| Bobby Bodenheimer | 9 | 2161 | 182.31 |