Development of deep learning algorithm for detection of colorectal cancer in EHR data

Yu Hsiang Wang; Phung Anh Nguyen; Md Mohaimenul Islam; Yu Chuan Li; Hsuan Chia Yang

doi:10.3233/SHTI190259

Development of deep learning algorithm for detection of colorectal cancer in EHR data

Yu Hsiang Wang
, Phung Anh Nguyen
, Md Mohaimenul Islam
, Yu Chuan Li
, Hsuan Chia Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

40 Citations (Scopus)

Abstract

We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

Original language	English
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	438-441
Number of pages	4
Volume	264
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190259
Publication status	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	8/25/19 → 8/30/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Algorithms
Colorectal Neoplasms
Electronic Health Records

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI190259

Cite this

Wang, Y. H., Nguyen, P. A., Mohaimenul Islam, M., Li, Y. C., & Yang, H. C. (2019). Development of deep learning algorithm for detection of colorectal cancer in EHR data. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (Vol. 264, pp. 438-441). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190259

Development of deep learning algorithm for detection of colorectal cancer in EHR data. / Wang, Yu Hsiang; Nguyen, Phung Anh; Mohaimenul Islam, Md et al.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. Vol. 264 IOS Press, 2019. p. 438-441 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, YH, Nguyen, PA, Mohaimenul Islam, M, Li, YC & Yang, HC 2019, Development of deep learning algorithm for detection of colorectal cancer in EHR data. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. vol. 264, Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 438-441, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190259

Wang YH, Nguyen PA, Mohaimenul Islam M, Li YC , Yang HC. Development of deep learning algorithm for detection of colorectal cancer in EHR data. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Vol. 264. IOS Press. 2019. p. 438-441. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190259

Wang, Yu Hsiang ; Nguyen, Phung Anh ; Mohaimenul Islam, Md et al. / Development of deep learning algorithm for detection of colorectal cancer in EHR data. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. Vol. 264 IOS Press, 2019. pp. 438-441 (Studies in Health Technology and Informatics).

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abstract = "We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.",

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note = "Funding Information: This research was sponsored in part by Ministry of Science and Technology (MOST) 107-2634-F-038-002-, and Ministry of Science and Technology (MOST) 106-2634-F-038 -001 - CC2. We would like to thank Mr. Chia-Wei Liang for his assistant in this study. Publisher Copyright: {\textcopyright} 2019 International Medical Informatics Association (IMIA) and IOS Press.; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",

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N1 - Funding Information: This research was sponsored in part by Ministry of Science and Technology (MOST) 107-2634-F-038-002-, and Ministry of Science and Technology (MOST) 106-2634-F-038 -001 - CC2. We would like to thank Mr. Chia-Wei Liang for his assistant in this study. Publisher Copyright: © 2019 International Medical Informatics Association (IMIA) and IOS Press.

PY - 2019/8/21

Y1 - 2019/8/21

N2 - We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

AB - We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal cancer in Taiwanese adults. We collected data of 58152 patients from the Taiwan National Health Insurance database from 1999 to 2013. All patients' comorbidities and medications history were included in the development of the convolution neural network (CNN) model. We also used 3-year medical data of all patients before the diagnosed colorectal cancer (CRC) as the dimensional time in the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were computed to measure the performance of the model. The results showed the mean (SD) of AUC of the model was 0.922 (0.004). Moreover, the performance of the model observed the sensitivity of 0.837, specificity of 0.867, and 0.532 for PPV value. Our study utilized CNN to develop a prediction model for CRC, based on non-image and multi-dimensional medical records.

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Development of deep learning algorithm for detection of colorectal cancer in EHR data

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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