ArticleArtificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )2017
Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.
Article in Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Representation Learning for Fine-Grained Change Detection.Sensors (Basel, Switzerland) · 2021Review
- Prognostic models will be victims of their own success, unless….Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Smashing the strict hierarchy: three cases of clinical decision support malfunctions involving carvedilol.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.JMIR medical informatics · 2018Article
- Balancing Performance and Interpretability: Selecting Features with Bootstrapped Ridge Regression.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Agile Co-Development for Clinical Adoption and Adaptation of Innovative Technologies.... Health innovations and point-of-care technologies conference. Health innovations and point-of-care technologies conference · 2017Article
- Change-Point Detection for Monitoring Clinical Decision Support Systems with a Multi-Process Dynamic Linear Model.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.