Recent Advances in Predictive Modeling with Electronic Health Records
Jiaqi Wang Junyu Luo Muchao Ye Xiaochen Wang Yuan Zhong Aofei Chang Guanjie Huang Ziyi Yin Cao Xiao Jimeng Sun Fenglong Ma
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.