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目的:将人工智能技术应用于原发性肝细胞癌患者的临床真实世界数据研究，探索肝癌精准治疗，建立肝癌人工智能临床决策支持系统。方法:将2004年7月至2016年6月间华西医院收治且有完整随访记录的5 642例原发性肝癌患者纳入研究。采用多分类器融合模型计算治疗方案推荐系数，并分析受试者工作特征曲线；采用DeepSurv算法实现生存风险和复发风险的预测，并进一步对比低风险组、中风险组和高风险组间的Kaplan-Meier生存曲线；利用Siamese-Net算法得到相似病例结果。结果:治疗方案推荐系数的Top-1准确率和Top-2准确率分别为82.36%和94.13%；在华西医院内部使用验证过程中，与多学科会诊治疗方案的匹配准确率达95.10%。生存风险模型得到的C-index值为0.735（95n %CI：0.70~0.77），各风险组的Kaplan-Meier曲线经log-rank检验，各组间差异有统计学意义（n P<0.001）。复发风险模型得到的C-index值为0.705（95n %CI：0.68~0.73），各风险组的Kaplan-Meier曲线经log-rank检验，各组间差异有统计学意义（n P<0.001）。n 结论:肝癌人工智能临床决策支持系统能较为准确地进行原发性肝细胞癌治疗方案推荐和治疗预后预测。“,”Objective:To apply artificial intelligence technology in clinical real-world data of patients with primary hepatocellular carcinoma, explore the precise treatment of disease and build up artificial intelligence-based clinical decision support system.Methods:A total of 5 642 patients with primary hepatocellular carcinoma admitted to West China Hospital from July 2004 to June 2016 with complete follow-up records were included in the study. A merged model composed of multiple sub-classifiers was adopted to calculate therapy recommendation coefficient, and receiver operator characteristic curve was analyzed. Survival risk and recurrence risk were predicted by DeepSurv algorithm, and Kaplan-Meier survival curves were further compared among low, middle and high risk groups. Siamese-Net was applied to find similar patients.Results:The Top-1 and Top-2 accuracy of therapy recommendation coefficient reached 82.36% and 94.13% respectively. In internal verification of West China Hospital, the above-mentioned value reached 95.10% in accordance with multi-disciplinary team results. The C-index derived from survival risk model was 0.735 (95n %CI:0.70-0.77), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (n P<0.001). Meanwhile, the C-index derived from recurrence risk model was 0.705 (95n %CI:0.68-0.73), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (n P<0.001).n Conclusions:The artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma has can accurately make therapy recommendation and prognosis prediction for primary hepatocellular carcinoma.