A Hybrid AI Framework to Address the Issue of Frequent Missing Values with Application in EHR Systems: the Case of Parkinson’s Disease
A Hybrid AI Framework to Address the Issue of Frequent Missing Values with Application in EHR Systems: the Case of Parkinson’s Disease
Files
Date
2024-01-03
Authors
Amini, Mostafa
Bagheri, Ali
Piri, Saeed
Delen, Dursun
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
1040
Ending Page
Alternative Title
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.
Description
Keywords
Data Science and Machine Learning to Support Business Decisions,
clinical decision support systems,
explainable ai,
feature selection,
missing values,
parkinson’s disease
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.