摘要
一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-中枢神经系统疾病和状况的新研究-脑出血是一篇报道的主题。根据NewsRx Edito RS在中华人民共和国百色的新闻报道，研究表明：“ICH患者的复发会恶化病情，增加死亡率。预测复发风险并预防或治疗这些患者是潜在改善预后的合理策略。”我们的新闻记者引用右江民族医科大学附属医院的一篇研究文章:“提高机器学习模型的性能是预测复发的必要条件。我们收集了两家医院的ICH患者的回顾性训练队列和PR检测队列的数据,结果是1年内复发。”我们构造了Logistic回归,支持向量机(SVM),决策树、Voti NG分类器、随机森林和XGBoost模型进行预测。该模型包括年龄、出院时NIHSS评分、入院和出院时血肿体积、入院时P LT、AST和CRP水平、低血压药物使用情况和ST Roke病史。在内部验证中，Logistic回归显示AUC为0.89，D精度为0.81，SVM显示AUC随机森林的AUC为0.95，精密度为0.93，XGBoost外部验证的AUC为0.95，精密度为0.92.，Logistic回归的AUC为0.81，精密度为0.79，SVM的AUC为0.87，精密度为0.76，随机森林的AUC为0.92，精密度为0.86.D XGBoost记录的AUC为0.93，精度为0.91.。机器学习模型在预测颅内出血复发方面优于传统的统计模型。
Abstract
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."
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