Summary of Recent Advances in Predictive Modeling with Electronic Health Records, by Jiaqi Wang et al.
Recent Advances in Predictive Modeling with Electronic Health Records
by Jiaqi Wang, Junyu Luo, Muchao Ye, Xiaochen Wang, Yuan Zhong, Aofei Chang, Guanjie Huang, Ziyi Yin, Cao Xiao, Jimeng Sun, Fenglong Ma
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in electronic health records (EHR) has led to the accumulation of a massive amount of digitized patient data, offering opportunities for predictive modeling. However, leveraging EHR data for predictive tasks poses unique challenges due to its characteristics. Notably, deep learning techniques have shown superior performance across various applications, including healthcare. This survey provides an in-depth review of recent advancements in deep learning-based predictive models utilizing EHR data. We first introduce the background on EHR data and define the predictive modeling task mathematically. Subsequently, we categorize and summarize predictive deep models from multiple perspectives. Additionally, 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how doctors can use special computer programs called deep learning models to predict what will happen to patients based on their health records. Right now, there are a lot of electronic health records (EHR) that have been collected from hospitals and clinics. But using this data to make predictions is hard because it’s not like other types of data. The paper talks about how deep learning can be used in healthcare and looks at some recent advances in this area. It also discusses the challenges and possibilities for making better predictions in the future. |
Keywords
* Artificial intelligence * Deep learning
