PDF(Pubmed)
Abstract：
This study intends to use the basic information and blood routine of schistosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022. The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the optimal model was compared based on AUC, specificity, sensitivity and other indicators for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients\' medical records were collected, and 10 key variables were screened for modeling using lasso method, including red cell distribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin concentration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cells, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the training set and validation set were 1 and 0.818, respectively. This study established a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum. The model could help improve the early diagnosis and provide early intervention for schistosomiasis patients with liver fibrosis.
摘要：
本研究拟利用血吸虫病患者的基本信息和血常规,建立预测肝纤维化的机器学习模型。我们收集了2019年6月至2022年6月中国一家医院收治的日本血吸虫患者的病历。该方法是筛选出关键变量，并使用六种不同的机器学习算法来建立预测模型。最后,基于AUC比较了最优模型，特异性，灵敏度和其他指标，用于进一步建模。通过使用SHAP包显示了模型的解释。共收集了1049名患者的医疗记录，并筛选了10个关键变量，使用套索方法进行建模，包括红细胞分布宽度-标准偏差(RDW-SD)，平均红细胞血红蛋白浓度（MCHC），平均红细胞体积（MCV），血细胞比容(HCT),红细胞,嗜酸性粒细胞,单核细胞,淋巴细胞,中性粒细胞,年龄。在6种不同的机器学习算法中，LightGBM表现最好，其在训练集和验证集中的AUC分别为1和0.818。本研究建立了预测日本血吸虫肝纤维化的机器学习模型。该模型有助于提高血吸虫病肝纤维化患者的早期诊断和早期干预。
