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测试机器学习模型,以预测结直肠手术后的术后阴茎

Garry Brydges¹, George J Chang², Tong J Gan¹

¹Division of Anesthesiology, Critical Care & Pain Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Current oncology (Toronto, Ont.)

|June 26, 2024

概括

机器学习模型准确地预测了结直肠外科手术患者的手术后大肠风险 (POI). 这些模型识别了关键的并发症,为早期检测提供了一种新方法,并改善了患者的治疗结果.

科学领域:

结肠直肠手术结肠直肠手术
手术并发症手术并发症
机器学习在医学中的应用

背景情况:

手术后阴道 (POI) 是结直肠手术后的常见并发症.
POI显著增加了住院时间和相关的医疗费用.
确定POI的预测因素对于优化患者护理至关重要.

研究的目的:

在结直肠外科手术中调查与POI发展相关的患者并发症.
为了评估机器学习 (ML) 模型在预测POI时的准确性.
将ML模型的性能与既有风险评估工具进行比较.

主要方法:

对316名成年结直肠手术患者的回顾性分析 (2020年1月至2021年12月).
排除标准包括复杂的切除和不充分的随访.
八个ML模型经过训练和验证,使用29种并发症和4种风险指数.

主要成果:

POI的发生率为6.33%.
POI的重要预测因素包括年龄,BMI,性别,病,贫血,心律不整,类风湿关节炎和NSQIP得分.
AdaBoost (94.2%) 和XG Boost (85.2%) 显示出最高的预测准确度.

结论:

在结直肠手术后,ML模型在预测POI风险方面表现出高准确度.
这些模型代表了POI早期检测和干预的潜在进步.
改善POI预测可以改善患者的治疗结果,并降低医疗保健成本.

关键词:

人工智能的人工智能是人工智能.结肠直肠癌是什么意思这种疾病是共患的.机器学习是机器学习.神经网络的神经网络的神经网络在手术后出现的阴.

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