Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer

Mohaimenul Islam; Hsuan Chia Yang; Phung Anh Nguyen; Yu Hsiang Wang; Tahmina Nasrin Poly; Yu Chuan Jack Li

doi:10.3233/SHTI200382

Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer

Mohaimenul Islam, Hsuan Chia Yang, Phung Anh Nguyen, Yu Hsiang Wang, Tahmina Nasrin Poly, Yu Chuan Jack Li

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

Original language	English
Pages (from-to)	1241-1242
Number of pages	2
Journal	Studies in Health Technology and Informatics
Volume	270
DOIs	https://doi.org/10.3233/SHTI200382
Publication status	Published - Jun 16 2020

Keywords

Convolutional neural network
Deep learning
Electronic health record
Prostate cancer

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

UN SDGs

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

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10.3233/SHTI200382

Cite this

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title = "Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer",

abstract = "We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.",

keywords = "Convolutional neural network, Deep learning, Electronic health record, Prostate cancer",

author = "Mohaimenul Islam and Yang, {Hsuan Chia} and Nguyen, {Phung Anh} and Wang, {Yu Hsiang} and Poly, {Tahmina Nasrin} and Li, {Yu Chuan Jack}",

note = "Publisher Copyright: {\textcopyright} 2020 European Federation for Medical Informatics (EFMI) and IOS Press.",

year = "2020",

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doi = "10.3233/SHTI200382",

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T1 - Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer

AU - Islam, Mohaimenul

AU - Yang, Hsuan Chia

AU - Nguyen, Phung Anh

AU - Wang, Yu Hsiang

AU - Poly, Tahmina Nasrin

AU - Li, Yu Chuan Jack

PY - 2020/6/16

Y1 - 2020/6/16

N2 - We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

AB - We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

KW - Convolutional neural network

KW - Deep learning

KW - Electronic health record

KW - Prostate cancer

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Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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