TCTR20221018001已完成不适用
PREDICTION MODEL OF SURGICAL SITE INFECTION IN ABDOMINAL SURGERY: TRADITIONAL STATISTICAL MODELS VERSUS MACHINE LEARNING
Faculty of Medicine Ramathibodi Hospital, Department of Clinical Epidemiology and Biostatistics0 个研究点目标入组 12,596 人开始时间: 2022年10月18日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 12,596
研究概览
简要总结
An updated version of the SENIC score finally included blood transfusion, emergency operation, open approach, concurrent procedure, operation time, and diabetes as predictive factors. This model yielded an AUC (95% CI) of 0.768 (0.745, 0.790). AUCs from 4 machine learning models including decision tree, random forest, adaptive boosting, and naive Bayes were 0.681, 0.671, 0.518, and 0.677, respectively.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18 Years 至 N/A (No limit)（—）
- 性别
- All
入选标准
- •received a primary abdominal procedure including gastrointestinal, colorectal surgery, or a hernia repair.
排除标准
- 未提供
研究者
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