一种人工智能可解释的工具,用于预测静脉内热除后深静脉血栓形成的风险
Azadeh Tabari1, Yu Ma2, Jesus Alfonso2
1Department of Radiology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA.
概括
人工智能可以预测静脉缺陷的静脉内热切除术 (EVTA) 后深静脉血栓塞 (DVT) 风险. 这种工具有助于识别高风险患者进行早期干预,改善结果.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 内静脉热移除 (EVTA) 是一种常见的治疗表面静脉缺陷的方法.
- 血栓塞栓并发症虽然很少见,但在EVTA后可能会严重.
- 早期识别患有深静脉血栓症 (DVT) 高风险的患者至关重要.
研究的目的:
- 开发和验证人工智能 (AI) 模型,在EVTA后30天内预测DVT风险.
- 确定在EVTA后导致DVT风险的关键患者因素.
主要方法:
- 使用美国外科医生学院国家外科质量改善计划数据库 (2007-2017).
- 开发并验证了四种机器学习模型 (CART,OCT,随机森林,XGBoost) 使用患者人口统计学,并发病症和实验室值.
- 在模型解释性和特征重要性分析中使用了SHapley添加式解释 (SHAP).
主要成果:
- 包括21549名患者 (平均年龄54岁,67%为女性);1.59%患有DVT.
- 在预测DVT时,XGBoost模型表现出良好的区分能力 (AUC 0.711).
- 模型的性能在各种患者子组 (年龄,BMI,WBC,血小板计数) 中保持一致.
结论:
- 一个可解释的AI模型已成功开发和验证,用于预测EVTA后的DVT风险.
- 医生可以使用这个模型来识别患有表面静脉功能不全的患者,这些患者患有DVT的风险更高.
- 这有助于及时进行干预,并可能减少严重的血栓栓塞并发症.
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