Institute of Hepatology, The Third People's Hospital of Changzhou, Changzhou, 213001, China.
Department of Neurosurgery, The First People's Hospital of Changzhou, Changzhou, 213001, China.
Sci Rep. 2021 Mar 3;11(1):5081. doi: 10.1038/s41598-021-84556-4.
Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relationships in clinical data. Our study proposed a predictive model on the basis of 55 routine laboratory and clinical parameters by machine learning algorithms as a novel non-invasive method for liver fibrosis diagnosis. The model was further evaluated on the accuracy and rationality and proved to be highly accurate and efficient for the prediction of HBV-related fibrosis. In conclusion, we suggested a potential combination of high-dimensional clinical data and machine learning predictive algorithms for the liver fibrosis diagnosis.
慢性乙型肝炎病毒感染是肝硬化和肝细胞癌的主要病因，已成为全球关注的健康问题。机器学习算法在通过捕捉临床数据中的复杂和非线性关系来分析医学现象方面特别擅长。我们的研究提出了一种基于 55 项常规实验室和临床参数的预测模型，作为一种新的非侵入性肝纤维化诊断方法。该模型在准确性和合理性方面进行了进一步评估，证明对乙型肝炎病毒相关纤维化的预测具有高度准确性和效率。总之，我们建议将高维临床数据与机器学习预测算法相结合，用于肝纤维化诊断。
