Deep neural models for chronic disease status detection in free text clinical records

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)

Abstract

Timely identification or prediction of a disease status can be a boon to patient's life. The elements that can detect the diseases status are mostly present in free text clinical records; these records contain private information about the patients. The time-consuming manual process to identify the disease status from these clinical records can be error-prone and comes with an expense. Hence the need to automate or semi automate this process is felt in the community. In this paper, we have used deep learning techniques on the publically available i2b2 clinical datasets to detect the chronic disease status, leading to promising results.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsJeffrey Yu, Zhenhui Li, Hanghang Tong, Feida Zhu
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages864-869
Number of pages6
ISBN (Electronic)9781538692882
ISBN (Print)9781538692899
DOIs
Publication statusPublished - 17 Nov 2018
EventIEEE International Conference on Data Mining -
Duration: 20 Nov 2018 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November

Conference

ConferenceIEEE International Conference on Data Mining
Period20/11/18 → …

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  • Cite this

    Rajput, K., Chetty, G., & Davey, R. (2018). Deep neural models for chronic disease status detection in free text clinical records. In J. Yu, Z. Li, H. Tong, & F. Zhu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 864-869). [8637453] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2018.00127