Develop a Prediction Model for Nonmelanoma Skin Cancer Using Deep Learning in EHR Data

研究成果: 書貢獻/報告類型章節

4 引文 斯高帕斯(Scopus)

摘要

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’ 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.

原文英語
主出版物標題Studies in Computational Intelligence
發行者Springer Singapore
頁面11-18
頁數8
DOIs
出版狀態已發佈 - 2021

出版系列

名字Studies in Computational Intelligence
899

ASJC Scopus subject areas

  • 人工智慧

指紋

深入研究「Develop a Prediction Model for Nonmelanoma Skin Cancer Using Deep Learning in EHR Data」主題。共同形成了獨特的指紋。

引用此