首页|Chaohu Hospital of Anhui Medical University Reports Findings in Thrombocytopenia (Machine learning for identifying risk of death in patients with severe fever w ith thrombocytopenia syndrome)
Chaohu Hospital of Anhui Medical University Reports Findings in Thrombocytopenia (Machine learning for identifying risk of death in patients with severe fever w ith thrombocytopenia syndrome)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Hematologic Diseases a nd Conditions - Thrombocytopenia is the subject of a report. According to news r eporting from Hefei, People’s Republic of China, by NewsRx journalists, research stated, “Severe fever with thrombocytopenia syndrome (SFTS) has attracted atten tion due to the rising incidence and high severity and mortality rates. This stu dy aims to construct a machine learning (ML) model to identify SFTS patients at high risk of death early in hospital admission, and to provide early intensive i ntervention with a view to reducing the risk of death.” The news correspondents obtained a quote from the research from the Chaohu Hospi tal of Anhui Medical University, “Data of patients hospitalized for SFTS in two hospitals were collected as training and validation sets, respectively, and six ML methods were used to construct the models using the screened variables as fea tures. The performance of the models was comprehensively evaluated and the best model was selected for interpretation and development of an online web calculato r for application. A total of 483 participants were enrolled in the study and 96 (19.88%) patients died due to SFTS. After a comprehensive evaluati on, the XGBoost-based model performs best: the AUC scores for the training and v alidation sets are 0.962 and 0.997. Using ML can be a good way to identify high risk individuals in SFTS patients.”
HefeiPeople’s Republic of ChinaAsiaBlood Platelet DisordersCyborgsEmerging TechnologiesHealth and MedicineHematologic Diseases and ConditionsHematologyMachine LearningRisk and Prev entionThrombocytopenia
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