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

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dc.contributor.authorAkshara P.
dc.contributor.authorShidharth S.
dc.contributor.authorKrishnan G.S.
dc.contributor.authorSowmya Kamath S.
dc.date.accessioned2021-05-05T10:15:57Z
dc.date.available2021-05-05T10:15:57Z
dc.date.issued2020
dc.description.abstractThe 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. © 2021 Owner/Author.en_US
dc.identifier.citationACM International Conference Proceeding Series , Vol. , , p. 436 -en_US
dc.identifier.urihttps://doi.org/10.1145/3430984.3431060
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/14893
dc.titleIntegrating Structured and Unstructured Patient Data for ICD9 Disease Code Group Predictionen_US
dc.typeConference Paperen_US

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