Preprint
Recent Advances in Predictive Modeling with Electronic Health Records
ArXiv.org
Cornell University
02/02/2024
DOI: 10.48550/arxiv.2402.01077
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 begin by introducing the background of EHR data and providing 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 WangJunyu LuoMuchao YeXiaochen WangYuan ZhongAofei ChangGuanjie HuangZiyi YinCao XiaoJimeng SunFenglong Ma
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2402.01077
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 02/02/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984696689502771
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