Name
Title | ||
|---|---|---|
Practical Development And Operationalization Of A 12-Hour Hospital Census Prediction Algorithm |
Abstract | ||
|---|---|---|
Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital's decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/- 3.4% and that this work could be completed in approximately 7 months. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/jamia/ocab089 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | DocType | Volume |
machine learning, forecasting, hospital census, inpatient | Journal | 28 |
Issue | ISSN | Citations |
9 | 1067-5027 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander J Ryu | 1 | 1 | 0.37 |
| Santiago Romero-Brufau | 2 | 1 | 1.05 |
| Narges Shahraki | 3 | 1 | 0.37 |
| Jiawei Zhang | 4 | 1 | 0.37 |
| Ray Qian | 5 | 1 | 0.37 |
| Thomas C Kingsley | 6 | 1 | 0.37 |