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Outlier Detection in Health Record Free-Text using Deep Learning.

Duncan Wallace, Tahar Kecahdi

    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 a neural network framework for analyzing complex Electronic Health Records (EHR) data. The system effectively predicts outlier cases, achieving high accuracy in patient classification.

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    Area of Science:

    • Computational biology
    • Medical informatics
    • Machine learning

    Background:

    • Classic machine learning struggles with unstructured Electronic Health Records (EHR) data.
    • Free-text clinical notes offer analytical potential but pose challenges.
    • Decentralized solutions are needed due to dispersed health data.

    Purpose of the Study:

    • To develop a neural network framework for patient classification using heterogeneous, incomplete, and noisy EHR data.
    • To predict outlier cases, specifically frequent attender patients.

    Main Methods:

    • Development of a neural network framework tailored for EHR environments.
    • Handling of data that is heterogeneous, incomplete, and noisy.
    • Implementation of a system for predicting outlier patient cases.

    Main Results:

    • The developed system achieves an Area-Under-the-Curve (AUC) score of up to 0.92.
    • Successful classification of patient data within a challenging EHR environment.

    Conclusions:

    • Neural networks offer a viable approach for analyzing complex EHR data.
    • The framework demonstrates effectiveness in identifying outlier patient cases.
    • This work advances machine learning applications in clinical informatics.