Chapter title |
Change-Point Detection Method for Clinical Decision Support System Rule Monitoring
|
---|---|
Chapter number | 14 |
Book title |
Artificial Intelligence in Medicine
|
Published in |
Artificial intelligence in medicine : 16th Conference on Artificial Intelligence in Medicine, Aime 2017, Vienna, Austria, June 21-24, 2017, Proceedings. Conference on Artificial Intelligence in Medicine (2005-) (16th : 2017 : Vienna, Au..., June 2017
|
DOI | 10.1007/978-3-319-59758-4_14 |
Pubmed ID | |
Book ISBNs |
978-3-31-959757-7, 978-3-31-959758-4
|
Authors |
Siqi Liu, Adam Wright, Milos Hauskrecht |
Abstract |
A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 10 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 3 | 30% |
Professor | 2 | 20% |
Student > Doctoral Student | 1 | 10% |
Student > Bachelor | 1 | 10% |
Student > Master | 1 | 10% |
Other | 1 | 10% |
Unknown | 1 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 3 | 30% |
Engineering | 2 | 20% |
Medicine and Dentistry | 2 | 20% |
Psychology | 1 | 10% |
Unknown | 2 | 20% |