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.
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通过电子健康记录中的最小特征来预测肝细胞癌：开发深度学习模型。
作者：
Liang CW;Yang HC;Islam MM;Nguyen PAA;Feng YT;Hou ZY;Huang CW;Poly TN;Li YJ
肝细胞癌（HCC），通常被称为肝癌，是全球癌症死亡的第三大原因。早期发现肝癌有助于治疗并提高生存率。本研究的目的是开发一个深度学习模型，利用电子健康记录中每个医疗事件的趋势和严重程度来准确预测1年后将被诊断为HCC的患者。HCC患者必须在灾难性疾病档案中登记为癌症患者，并在住院时被诊断为HCC患者。对照病例（非hcc患者）从同一数据库中随机抽取。我们使用年龄、性别、诊断代码、药物代码和时间信息作为卷积神经网络模型的输入变量来预测这些HCC患者。我们还检查了模型中的高权重变量，并将其与HCC的优势比进行比较，以了解预测模型是如何工作的。我们纳入了47,945个人，其中9553人是HCC患者。该模型提前1年预测HCC风险的受试者工作曲线下面积（AUROC）为0.94 (95% CI 0.937 ~ 0.943)，敏感性0.869，特异性0.865。预测HCC患者早期7天、6个月、1年、2年和3年的AUROC分别为0.96、0.94、0.94、0.91和0.91。这项研究的结果表明，卷积神经网络模型具有巨大的潜力，可以提前1年预测HCC的风险，而电子健康记录中可用的特征最少。
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. 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. 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 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. 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.