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Integrating Structured and Unstructured Patient Data for ICD9 Disease Code Group Prediction

Published:02 January 2021Publication History

ABSTRACT

The large-scale availability of healthcare data provides significant opportunities for development of advanced Clinical Decision Support Systems that can enhance patient care. One such essential application is automated ICD-9 diagnosis group prediction, useful for a variety of healthcare delivery related tasks including documenting, billing and insurance claims. Past attempts considered patients’ multivariate lab events data and clinical text notes independently. To the best of our knowledge, ours is the first attempt to investigate the efficacy of integration of both these aspects for this task. Experiments on MIMIC-III dataset showed promising results.

References

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  • Published in

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    CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
    January 2021
    453 pages

    Copyright © 2021 Owner/Author

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 2 January 2021

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    Overall Acceptance Rate197of680submissions,29%

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