使用机器学习 (ML) 模型来预测脊椎手术后静脉血栓栓塞 (VTE) 的风险
Prerana Katiyar1, Herbert Chase2, Lawrence G Lenke2,3
1Columbia University Vagelos College of Physicians and Surgeons.
Clinical spine surgery
|July 24, 2023
概括
机器学习模型有效预测脊椎手术患者的静脉血栓栓塞 (VTE) 风险. 这些模型识别非传统的风险因素,改进了现有的VTE风险评估方法.
科学领域:
- 整形外科 整形外科 整形外科
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 静脉血栓塞栓症 (VTE) 是脊椎手术患者的重大风险.
- 现有的VTE风险评估指南可能不包括所有相关因素.
- 需要新的方法来提高该人群的VTE风险预测.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测脊椎手术患者的静脉瘤.
- 确定与脊椎手术后静脉瘤发育相关的非传统风险因素.
- 为了提高VTE风险预测的准确性,超越目前的方法.
主要方法:
- 一个回顾性队列研究,对63名脊椎外科手术患者 (31名有VTE,32名没有).
- 收集和分析113个属性,包括人口统计,药物,实验室和病史.
- 使用怀卡托环境知识分析 (WEKA) 软件开发和评估6个ML模型,具有10倍的交叉验证.
主要成果:
- 所有的ML模型在预测VTE风险方面显著优于对照模型 (ZeroR) (P <0.001).
- 随机森林模型获得了最高的准确度 (88.89%),预测值为93.75%.
- 确定的主要预测因素包括实验室值 (,酸盐,血红蛋白),并发症,药物使用和麻醉时间.
结论:
- 机器学习模型显示,在脊椎手术患者中预测静脉瘤风险的潜力很高.
- 这些模型可以识别新的风险因素,提供增强的诊断价值.
- 进一步的发展可以指导高风险个体的向化疗预防策略.
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