@inbook{cdbe5eddb0f04a948af80caa9b679c79,
title = "Develop a Prediction Model for Nonmelanoma Skin Cancer Using Deep Learning in EHR Data",
abstract = "We aimed to develop deep learning models for the prediction of the risk of advanced nonmelanoma skin cancer (NMSC) in Taiwanese adults. We collected the data of 9494 patients from Taiwan National Health Insurance data claim from 1999 to 2013. All patients{\textquoteright} diseases and medications were included in the development of the convolution neural network (CNN) model. We used the 3-year medical data of all patients before the diagnosed NMSC 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 0.894 (0.007). The performance of the model observed with the sensitivity of 0.83, specificity of 0.82, and 0.57 for PPV value. Our study utilized CNN to develop a prediction model for NMSC, based on non-image and multi-dimensional medical records.",
author = "Huang, {Chih Wei} and Nguyen, {Alex P.A.} and Wu, {Chieh Chen} and Yang, {Hsuan Chia} and Li, {Yu Chuan (Jack)}",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-49536-7_2",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Singapore",
pages = "11--18",
booktitle = "Studies in Computational Intelligence",
address = "Singapore",
}
