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Detecting Anomalies in Alert Firing within Clinical Decision Support Systems using Anomaly/Outlier Detection Techniques

Published: 02 October 2016 Publication History

Abstract

Clinical Decision Support (CDS) systems play an integral role in the improvement of health care quality and safety. Alert malfunctions within CDS are a common problem and these greatly limit its usability. Anomaly detection is a novel approach to identify malfunctioning within CDS systems. Once an anomaly in alert firing is detected, it can be used to rectify and potentially fix the CDS system to make it robust and reliable. We introduce and apply four anomaly detection algorithms to estimate the dates when malfunctions and/or changes in alert firing occur. Preliminary results demonstrate successful detection of anomaly occurrences within the alert data; this carries the potential to be used for root cause analysis of such malfunctions.

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  • (2023)Similarity Distribution Density: An Optimized Approach to Outlier DetectionElectronics10.3390/electronics1220422712:20(4227)Online publication date: 12-Oct-2023
  • (2021)Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their ClientsFinance: Theory and Practice10.26794/2587-5671-2020-25-5-186-19925:5(186-199)Online publication date: 28-Oct-2021
  • (2021)Teaching Analytics Medical-Data Common SenseHeterogeneous Data Management, Polystores, and Analytics for Healthcare10.1007/978-3-030-71055-2_14(171-187)Online publication date: 4-Mar-2021
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    cover image ACM Conferences
    BCB '16: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
    October 2016
    675 pages
    ISBN:9781450342254
    DOI:10.1145/2975167
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    Publication History

    Published: 02 October 2016

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    Author Tags

    1. Anomaly/outlier Detection
    2. Clinical Decision Support
    3. Time Series

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    Overall Acceptance Rate 254 of 885 submissions, 29%

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    Cited By

    View all
    • (2023)Similarity Distribution Density: An Optimized Approach to Outlier DetectionElectronics10.3390/electronics1220422712:20(4227)Online publication date: 12-Oct-2023
    • (2021)Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their ClientsFinance: Theory and Practice10.26794/2587-5671-2020-25-5-186-19925:5(186-199)Online publication date: 28-Oct-2021
    • (2021)Teaching Analytics Medical-Data Common SenseHeterogeneous Data Management, Polystores, and Analytics for Healthcare10.1007/978-3-030-71055-2_14(171-187)Online publication date: 4-Mar-2021
    • (2019)A clustering approach for detecting implausible observation values in electronic health records dataBMC Medical Informatics and Decision Making10.1186/s12911-019-0852-619:1Online publication date: 23-Jul-2019
    • (2019)Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2019.29219127(81664-81681)Online publication date: 2019
    • (2017)Applying Bayesian Changepoint Model and Hierarchical Divisive Model for Detecting Anomalies in Clinical Decision Support Alert FiringProceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics10.1145/3107411.3108200(592-592)Online publication date: 20-Aug-2017

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