首页|Affiliated Hospital of Youjiang Medical University for Nationalities Reports Fin dings in Cerebral Hemorrhage (Predicting the recurrence of spontaneous intracere bral hemorrhage using a machine learning model)
Affiliated Hospital of Youjiang Medical University for Nationalities Reports Fin dings in Cerebral Hemorrhage (Predicting the recurrence of spontaneous intracere bral hemorrhage using a machine learning model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Cerebral Hemorrhage is the subject of a report. Accor ding to news reporting out of Baise, People's Republic of China, by NewsRx edito rs, research stated, "Recurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these p atients is a rational strategy to improve outcomes potentially." Our news journalists obtained a quote from the research from the Affiliated Hosp ital of Youjiang Medical University for Nationalities, "A machine learning model with improved performance is necessary to predict recurrence. We collected data from ICH patients in two hospitals for our retrospective training cohort and pr ospective testing cohort. The outcome was the recurrence within one year. We con structed logistic regression, support vector machine (SVM), decision trees, Voti ng Classifier, random forest, and XGBoost models for prediction. The model inclu ded age, NIHSS score at discharge, hematoma volume at admission and discharge, P LT, AST, and CRP levels at admission, use of hypotensive drugs and history of st roke. In internal validation, logistic regression demonstrated an AUC of 0.89 an d precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achi eved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and preci sion of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, an d XGBoost recorded an AUC of 0.93 and precision of 0.91. The machine learning mo dels performed better in predicting ICH recurrence than traditional statistical models."
BaisePeople's Republic of ChinaCentr al Nervous System Diseases and ConditionsCerebral HemorrhageCyborgsEmergin g TechnologiesHealth and MedicineMachine LearningSupport Vector Machines
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