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Related Concept Videos

Current Trends in Nursing II01:30

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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction.

Varadraj P Gurupur, Muhammed Shelleh, Christopher Leone

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    Summary
    This summary is machine-generated.

    Telehealth natural language processing (NLP) can now analyze patient sentiment and data gaps. This AI-driven approach, THNN, enhances diagnostic accuracy and improves patient care quality in telemedicine.

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    Area of Science:

    • Medical Informatics
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Telemedicine is increasingly used for patient-physician communication.
    • Clinical data and patient sentiment are often missing in telehealth encounters.
    • Incomplete data can hinder accurate diagnosis and patient care.

    Purpose of the Study:

    • To address data incompleteness in telehealth.
    • To improve the quality of care in telemedicine through data analysis.
    • To develop a system for analyzing patient sentiment and data gaps in telehealth.

    Main Methods:

    • Utilized an ensemble of Natural Language Processing (NLP) and AI-enabled systems.
    • Developed THNN (Telehealth Natural Language Processing) for sentiment and incompleteness mapping.
    • Processed telehealth natural language data.

    Main Results:

    • THNN effectively maps patient sentiments and identifies data incompleteness.
    • The system provides seamless results for analysis.
    • Demonstrated potential for improved healthcare outcomes.

    Conclusions:

    • THNN offers a solution for managing incomplete data in telehealth.
    • Analyzing patient sentiment and data gaps enhances diagnostic capabilities.
    • This approach can lead to better quality of care and improved patient outcomes.