Using recurrent neural networks to predict colorectal cancer among patients

Ryan Amirkhan, Mark Hoogendoorn, Mattijs E. Numans, Leon Moons

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Development of predictive models from Electronic Medical Records (EMRs) is a far from trivial task. Especially the temporal nature of health records is an aspect that is often ignored yet of utmost importance. Additionally, data is extremely sparse. Previous research has shown that the identification of temporal patterns from EMR data can be highly beneficial in the prediction of colorectal cancer (CRC). In this paper, we try to apply recurrent neural networks, and more specifically Long Short Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) to see whether these networks could learn such valuable temporal patterns themselves and generate accurate predictive models for CRC. Results show that we attain performance on par with state-of-the-art algorithms (while being outperformed by one). The eventual Area under the ROC Curve (AUC) obtained is 0.811.

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
Volume2018-January
ISBN (Electronic)9781538627259
DOIs
Publication statusPublished - 5 Feb 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: 27 Nov 20171 Dec 2017

Conference

Conference2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period27/11/171/12/17

Fingerprint

Electronic medical equipment
Colorectal Cancer
Recurrent neural networks
Predictive Model
Recurrent Neural Networks
Electronics
Predict
Memory Term
Receiver Operating Characteristic Curve
Trivial
Health
Unit
Prediction
Long short-term memory

Cite this

Amirkhan, R., Hoogendoorn, M., Numans, M. E., & Moons, L. (2018). Using recurrent neural networks to predict colorectal cancer among patients. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280826
Amirkhan, Ryan ; Hoogendoorn, Mark ; Numans, Mattijs E. ; Moons, Leon. / Using recurrent neural networks to predict colorectal cancer among patients. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8
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Amirkhan, R, Hoogendoorn, M, Numans, ME & Moons, L 2018, Using recurrent neural networks to predict colorectal cancer among patients. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 27/11/17. https://doi.org/10.1109/SSCI.2017.8280826

Using recurrent neural networks to predict colorectal cancer among patients. / Amirkhan, Ryan; Hoogendoorn, Mark; Numans, Mattijs E.; Moons, Leon.

2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Amirkhan R, Hoogendoorn M, Numans ME, Moons L. Using recurrent neural networks to predict colorectal cancer among patients. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8 https://doi.org/10.1109/SSCI.2017.8280826