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Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model

Furthermore, Yang et al [20] developed a predictive model of HCC risk of over 5 or 10 years in advance in patients with chronic hepatitis B. Potential risk factors, including age, sex, alcohol consumption, and serum alanine aminotransferase level, were considered to develop and validate the predictive model. The regression model achieved AUROCs ranging from 82.1% to 88.5%, and the nomograms model achieved AUROCs ranging from 82.1% to 86.6%.

Chia-Wei Liang, Hsuan-Chia Yang, Md Mohaimenul Islam, Phung Anh Alex Nguyen, Yi-Ting Feng, Ze Yu Hou, Chih-Wei Huang, Tahmina Nasrin Poly, Yu-Chuan Jack Li

JMIR Cancer 2021;7(4):e19812

Correction: Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Correction: Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Due to a system error, the name of one author, Marvin Chia-Han Yeh, was replaced with the name of another author on the paper, Hsuan-Chia Yang. As well, the formatting of the author name "Yu-Chuan (Jack) Li" has been changed to "Yu-Chuan Jack Li" in the corrected version of the paper. In the originally published paper, the order of authors was listed as follows: Hsuan-Chia Yang, Yu-Hsiang Wang, Hsuan-Chia Yang, Kuan-Jen Bai, Hsiao-Han Wang, Yu-Chuan (Jack) Li.

Marvin Chia-Han Yeh, Yu-Hsiang Wang, Hsuan-Chia Yang, Kuan-Jen Bai, Hsiao-Han Wang, Yu-Chuan Jack Li

J Med Internet Res 2021;23(10):e33519