Conference proceeding
Recent Advances in Predictive Modeling with Electronic Health Records
PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024, pp.8272-8280
01/01/2024
DOI: 10.24963/ijcai.2024/914
Abstract
The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we introduce the background of EHR data and provide a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.
Details
- Title: Subtitle
- Recent Advances in Predictive Modeling with Electronic Health Records
- Creators
- Jiaqi Wang - Pennsylvania State UniversityJunyu Luo - Pennsylvania State UniversityMuchao Ye - Pennsylvania State UniversityXiaochen Wang - Pennsylvania State UniversityYuan Zhong - Pennsylvania State UniversityAofei Chang - Pennsylvania State UniversityGuanjie Huang - Pennsylvania State UniversityZiyi Yin - Pennsylvania State UniversityCao Xiao - IQVIAJimeng Sun - University of Illinois Urbana-ChampaignFenglong Ma - Pennsylvania State University
- Contributors
- K Larson (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024, pp.8272-8280
- Publisher
- International Joint Confereces on Artifical Intelligence Organization
- DOI
- 10.24963/ijcai.2024/914
- Number of pages
- 9
- Grant note
- 2238275 / National Science Foundation; National Science Foundation (NSF) R01AG077016 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Language
- English
- Date published
- 01/01/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984798359202771
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