机器学习模型的开发和内部验证,用于预测脊髓转移手术的患者的生存率
Borriwat Santipas1, Kanyakorn Veerakanjana2, Piyalitt Ittichaiwong2
1Department of Orthopaedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Asian spine journal
|May 20, 2024
概括
机器学习模型准确地预测脊髓转移手术后的生存率. 手术前血清白蛋白是关键因素,帮助外科医生做出治疗决策和患者护理.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 外科手术的结果
背景情况:
- 脊髓转移对于预测患者存活率提出了挑战.
- 传统的预后系统在准确度上有局限性.
- 机器学习提供了一种新的方法来提高预后能力.
研究的目的:
- 开发和评估用于预测脊髓转移患者生存的机器学习算法.
- 确定术后生存的关键预测因素.
- 为了改善接受脊柱息手术的患者的临床决策.
主要方法:
- 这是一项回顾性研究,使用了389名为脊髓转移而接受手术的患者的注册表 (2004-2018).
- 开发机器学习算法 (XGBoost,CatBoost) 来预测90日,180日和365天的生存期.
- 使用接收器操作特征曲线 (AUC) 下面的面积进行性能评估.
主要成果:
- XGBoost和CatBoost算法展示了强大的生存预测性能.
- 在XGBoost的研究中,AUC值为0.744 (180天) 和0.693 (365天).
- CatBoost在90天生存期达到0.758的AUC;手术前血清白蛋白是一个显著的预测因子.
结论:
- 机器学习模型对预测脊髓转移患者的生存有希望.
- 这些算法可以帮助外科医生选择治疗和评估风险.
- 在手术前确定与死亡率相关的因素可以提高个性化的患者护理.
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