{"type":"ScholarlyArticle","title":"DDL: Deep Dictionary Learning for Predictive Phenotyping","authors":[{"name":"Tianfan Fu","slug":"tianfan-fu","given":"Tianfan","family":"Fu","affiliation":"Department of Computational Science and Engineering , Georgia Institute of Technology"},{"name":"Trong Nghia Hoang","slug":"trong-nghia-hoang","given":"Trong Nghia","family":"Hoang","affiliation":"MIT-IBM Watson AI Lab , IBM Research"},{"name":"Cao Xiao","slug":"cao-xiao","given":"Cao","family":"Xiao","affiliation":"Analytics Center of Excellence , IQVIA"},{"name":"Jimeng Sun","slug":"jimeng-sun","given":"Jimeng","family":"Sun","affiliation":"Department of Computational Science and Engineering , Georgia Institute of Technology"}],"abstract":"Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. Several deep learning (DL) models have demonstrated great performance in predictive phenotyping. However, these DL-based phenotyping models requires access to a large amount of labeled data, which are often  expensive to acquire. To address this label-insufficient challenge, we propose a deep dictionary learning framework (DDL) for phenotyping, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation. With extensive experiments on multiple real-world EHR datasets, we demonstrated DDL can outperform the state of the art predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping such as heart failure classification, mortality prediction, and sequential prediction. All empirical results consistently show that unlabeled data can indeed be used to generate better data representation, which helps improve DDL's phenotyping performance over existing baseline methods that only uses labeled data.","doi":"10.24963/ijcai.2019/812","url":"https://ijcai.org/proceedings/2019/812","pdf":"https://ijcai.org/proceedings/2019/0812.pdf","markdown":"https://ijcai.org/proceedings/2019/812.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/812","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":812,"pages":{"first":5857,"last":5863},"keywords":["Special Track on AI for Improving Human-Well Being: Health applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Human wellbeing (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: AI applications for Improving Human-Well Being (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Baytas et al., 2017] Inci Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil Jain, and Jiayu Zhou. 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