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| Content Provider | ACM Digital Library |
|---|---|
| Author | Mcclean, Sally Liu, Dongmei Young, Terry Wang, Zidong Liu, Xiaohui Eatock, Julie |
| Copyright Year | 2014 |
| Abstract | In this article, the expectation maximization (EM) algorithm is applied for modeling the throughput of emergency departments via available time-series data. The dynamics of emergency department throughput is developed and evaluated, for the first time, as a stochastic dynamic model that consists of the noisy measurement and first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, the model parameters, the actual throughput, as well as the noise intensity, can be identified simultaneously. Four real-world time series collected from an emergency department in West London are employed to demonstrate the effectiveness of the introduced algorithm. Several quantitative indices are proposed to evaluate the inferred models. The simulation shows that the identified model fits the data very well. |
| Starting Page | 1 |
| Ending Page | 16 |
| Page Count | 16 |
| File Format | |
| ISSN | 2158656X |
| e-ISSN | 21586578 |
| DOI | 10.1145/2544105 |
| Volume Number | 4 |
| Issue Number | 4 |
| Journal | ACM Transactions on Management Information Systems (TMIS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-12-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | EM algorithm Healthcare Emergency department Modeling Time series data |
| Content Type | Text |
| Resource Type | Article |
| Subject | Management Information Systems Computer Science |
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