TY  - JOUR
AU  - Liang, Chia-Wei
AU  - Yang, Hsuan-Chia
AU  - Islam, Md Mohaimenul
AU  - Nguyen, Phung Anh Alex
AU  - Feng, Yi-Ting
AU  - Hou, Ze Yu
AU  - Huang, Chih-Wei
AU  - Poly, Tahmina Nasrin
AU  - Li, Yu-Chuan Jack
PY  - 2021
DA  - 2021/10/28
TI  - Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model
JO  - JMIR Cancer
SP  - e19812
VL  - 7
IS  - 4
KW  - hepatocellular carcinoma
KW  - deep learning
KW  - risk prediction
KW  - convolution neural network
KW  - deep learning model
KW  - hepatoma
AB  - Background: Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. Objective: The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year. Methods: Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works Results: We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively. Conclusions: The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records. 
SN  - 2369-1999
UR  - https://cancer.jmir.org/2021/4/e19812
UR  - https://doi.org/10.2196/19812
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34709180
DO  - 10.2196/19812
ID  - info:doi/10.2196/19812
ER  -