首页|Central South University Reports Findings in Liver Fibrosis (Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum)
Central South University Reports Findings in Liver Fibrosis (Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum)
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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.”
HunanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineLiver Ci rrhosisLiver Diseases and ConditionsLiver FibrosisMachine LearningMedica l RecordsRecords as Topic
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