摘要
一位新闻记者兼机器人与机器学习每日新闻编辑每日新闻-肝脏疾病和疾病的新研究-肝纤维化是一篇报道的主题。据NewsRx记者报道，“本研究旨在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月中国某医院收治的日本血吸虫病患者的病历。”本研究经费来自国家自然科学基金。我们的新闻记者引用了中南大学研究的一句话:“方法是筛选关键变量,使用六种不同的机器学习算法建立预测模型,最后从AUC、特异性、敏感性等指标进行进一步建模，模型解释采用SHAP软件包进行，共收集1049份患者病历，采用LASO法筛选10个K ey变量进行建模，包括红细胞分布宽度-标准差(RDW-SD)、平均红细胞血红蛋白浓度(MCHC)、平均红细胞体积(MCV)、红细胞比容(HCT)、红细胞S、嗜酸性粒细胞、单核细胞、淋巴细胞、中性粒细胞、嗜酸性粒细胞、在6种不同的机器学习算法中，LightGBM表现最好，其在链集和验证集的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。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Liver Diseases and Con ditions - Liver Fibrosis is the subject of a report. According to news originati ng from Hunan, People’s Republic of China, by NewsRx correspondents, research st ated, “This study intends to use the basic information and blood routine of schi stosomiasis 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.” Financial support for this research came from National Natural Science Foundatio n of China. Our news journalists obtained a quote from the research from Central South Unive rsity, “The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the opti mal model was compared based on AUC, specificity, sensitivity and other indicato rs 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 k ey variables were screened for modeling using lasso method, including red cell d istribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin conce ntration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cell s, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the tr aining set and validation set were 1 and 0.818, respectively. This study establi shed a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.”
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