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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces the Clinical Event Annotator (CEA), a mobile health app for real-time clinical event annotation. The CEA app supports neonatal intensive care unit research by providing high-fidelity data for machine learning algorithm training.

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    Area of Science:

    • Biomedical Engineering
    • Clinical Informatics
    • Mobile Health (mHealth)

    Background:

    • Manual annotation of physiological data streams is time-consuming and prone to error.
    • Accurate annotation is crucial for training machine learning algorithms in clinical settings.
    • Existing methods lack dynamic updating capabilities for customized clinical events.

    Purpose of the Study:

    • To develop a novel dynamic mobile health (mHealth) application, the Clinical Event Annotator (CEA).
    • To support a clinical study investigating pressure-sensitive mats (PSM) in the neonatal intensive care unit (NICU).
    • To provide gold-standard annotations for training machine learning algorithms for clinical event detection.

    Main Methods:

    • Developed a native Android tablet app for real-time bedside annotation of clinical events (alarms, care, interventions, movements).
    • Created an administrative web app for generating annotation session reports.
    • Integrated the CEA app with a backend database for syncing annotations with independently acquired patient monitoring data (e.g., respiratory rate, heart rate, pressure data from PSM).
    • Enabled dynamic updates with user-defined customized events.

    Main Results:

    • The CEA app successfully performed real-time, high-fidelity annotation of clinical events.
    • Annotations were synchronized with patient monitoring data, including respiratory rate (RR), heart rate (HR), and pressure data from PSM.
    • Preliminary test results from the clinical study demonstrated the app's utility.
    • The CEA app provides a unique solution to the challenge of manual physiologic data stream annotation.

    Conclusions:

    • The Clinical Event Annotator (CEA) is a novel and dynamic mHealth application for real-time clinical event annotation.
    • The CEA app effectively supports clinical studies by providing accurate data for machine learning model development.
    • This tool addresses a significant gap in clinical data mining by streamlining the annotation process.