DDL: Deep dictionary learning for predictive phenotyping

Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. While deep learning (DL) models have recently demonstrated strong performance in predictive phenotyping, they require access to a large amount of labeled data, which are 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. Our empirical evaluations on multiple EHR datasets demonstrated that DDL outperforms the existing predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping. The results also show that unlabeled data can be used to generate better data representation that helps improve DDL's phenotyping performance over existing methods that only uses labeled data.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence
Pages5857-5863
Number of pages7
ISBN (Electronic)9780999241141
DOIs
StatePublished - Jan 1 2019
Externally publishedYes
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: Aug 10 2019Aug 16 2019

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2019-August
ISSN (Print)1045-0823

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
CountryChina
CityMacao
Period8/10/198/16/19

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Fu, T., Hoang, T. N., Xiao, C., & Sun, J. (2019). DDL: Deep dictionary learning for predictive phenotyping. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 5857-5863). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/812