{"type":"ScholarlyArticle","title":"Recent Advances in Predictive Modeling with Electronic Health Records","authors":[{"name":"Jiaqi Wang","slug":"jiaqi-wang","given":"Jiaqi","family":"Wang","affiliation":"Pennsylvania State University"},{"name":"Junyu Luo","slug":"junyu-luo","given":"Junyu","family":"Luo","affiliation":"Pennsylvania State University"},{"name":"Muchao Ye","slug":"muchao-ye","given":"Muchao","family":"Ye","affiliation":"Pennsylvania State University"},{"name":"Xiaochen Wang","slug":"xiaochen-wang","given":"Xiaochen","family":"Wang","affiliation":"Pennsylvania State University"},{"name":"Yuan Zhong","slug":"yuan-zhong","given":"Yuan","family":"Zhong","affiliation":"Pennsylvania State University"},{"name":"Aofei Chang","slug":"aofei-chang","given":"Aofei","family":"Chang","affiliation":"Pennsylvania State University"},{"name":"Guanjie Huang","slug":"guanjie-huang","given":"Guanjie","family":"Huang","affiliation":"Pennsylvania State University"},{"name":"Ziyi Yin","slug":"ziyi-yin","given":"Ziyi","family":"Yin","affiliation":"Pennsylvania State University"},{"name":"Cao Xiao","slug":"cao-xiao","given":"Cao","family":"Xiao","affiliation":"GE Healthcare"},{"name":"Jimeng Sun","slug":"jimeng-sun","given":"Jimeng","family":"Sun","affiliation":"University of Illinois Urbana-Champaign {jqwang , junyu , muchao , xcwang ,"},{"name":"Fenglong Ma","slug":"fenglong-ma","given":"Fenglong","family":"Ma","affiliation":"Pennsylvania State University"}],"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.","doi":"10.24963/ijcai.2024/914","url":"https://ijcai.org/proceedings/2024/914","pdf":"https://ijcai.org/proceedings/2024/0914.pdf","markdown":"https://ijcai.org/proceedings/2024/914.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/914","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":914,"pages":{"first":8272,"last":8280},"keywords":["Data Mining: DM: Applications","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Armanious et al., 2020] Karim Armanious, Chenming Jiang, Marc Fischer, Thomas Küstner, Tobias Hepp, Kon- stantin Nikolaou, Sergios Gatidis, and Bin Yang. 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