A Hybrid AI Framework to Address the Issue of Frequent Missing Values with Application in EHR Systems: the Case of Parkinson’s Disease

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dc.contributor.author Amini, Mostafa
dc.contributor.author Bagheri, Ali
dc.contributor.author Piri, Saeed
dc.contributor.author Delen, Dursun
dc.date.accessioned 2023-12-26T18:36:39Z
dc.date.available 2023-12-26T18:36:39Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other fa2f362d-410e-4b0e-8797-d852a906f6ca
dc.identifier.uri https://hdl.handle.net/10125/106503
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Science and Machine Learning to Support Business Decisions
dc.subject clinical decision support systems
dc.subject explainable ai
dc.subject feature selection
dc.subject missing values
dc.subject parkinson’s disease
dc.title A Hybrid AI Framework to Address the Issue of Frequent Missing Values with Application in EHR Systems: the Case of Parkinson’s Disease
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Electronic health record (EHR) systems hold vast amounts of patient data that, when analyzed with explainable AI techniques and predictive analytics, can improve clinical decision support systems (CDSS). However, the volume of data, with millions of patient records and hundreds of features collected over time, presents significant challenges, including handling missing values. In this project, we introduce a framework that addresses the issue of incompleteness in EHR data, enabling researchers to select the most important variables at an acceptable level of missing data to develop accurate predictive models. We demonstrate the effectiveness of this framework by applying it to developing a CDSS for detecting Parkinson's disease based on large EHR data. Parkinson's disease is hard to diagnose, and even specialists' diagnoses can be inaccurate; moreover, limited access to specialists in remote areas results in many undiagnosed patients. Our framework can be integrated into EHR systems or used as an independent tool by healthcare practitioners who are not necessarily specialists, bridging the gap in specialized care in remote areas. Our results show that the framework improves the accuracy of predictive models and identifies patients with Parkinson's disease who might otherwise go undiagnosed.
dcterms.extent 10 pages
prism.startingpage 1040
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Data Science and Machine Learning to Support Business Decisions