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基于机器学习的预测肠道阻塞手术后早期并发症:多中心回顾性研究
Pinjie Huang1, Jirong Yang1, Dizhou Zhao2
1Department of Anesthesiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Journal of medical Internet research
|March 7, 2025
概括
一个机器学习模型准确地预测了肠道阻塞手术后的早期并发症,有助于早期干预和改善患者的治疗结果. 该工具有助于识别高风险患者,以提供量身定制的治疗.
科学领域:
- 计算医学和外科瘤学.
- 开发和验证手术预测结果的预测模型.
背景情况:
- 肠道阻塞手术后的早期术后并发症与住院时间和死亡率的增加有关.
- 及时识别患有这些并发症高风险的患者对于实施预防性,个性化治疗至关重要.
- 机器学习为开发用于早期诊断和干预的准确风险预测模型提供了一个有希望的方法.
研究的目的:
- 构建和验证在线风险计算器,用于预测接受肠道阻塞手术的患者早期术后并发症.
- 利用机器学习算法开发一个强大的预测工具.
主要方法:
- 一组396名接受肠道阻塞手术的患者被用于培训,在多个数据集上进行外部验证.
- 评估了七个机器学习算法,使用包括AUROC,准确性,灵敏度,特异性和F1分数在内的指标来评估性能.
- 采用沙普利添加式解释 (SHAP) 来识别导致术后并发症的关键风险因素.
主要成果:
- 随机森林模型表现出卓越的性能,在培训队列中获得了0.788的AUROC,在外部验证队列中获得了相似的结果.
- 术后并发症的关键预测因素包括POSSUM生理评分,体输液量,冲击指数,ASA分类,中性粒细胞百分比,年龄和总蛋白质.
- 基于验证的随机森林模型开发了一个基于Web的在线风险计算器.
结论:
- 一个可概括的随机森林模型已成功开发和验证,用于预测肠道阻塞患者的早期术后并发症.
- 开发的在线风险计算器为临床医生提供了一种可访问的工具,用于识别高风险患者,促进早期,个性化的干预.
- 这种预测模型有可能通过及时和有针对性的治疗策略来显著改善患者的预后.
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