使用人工智能开发临床决策支持系统 - - 个性化瘤治疗的不断发展之路
Elena Chitoran1,2, Vlad Rotaru1,2, Aisa Gelal1,2
1Medicine School, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
Diagnostics (Basel, Switzerland)
|September 27, 2025
概括
这项研究开发了一种人工智能工具,用于预测贝瓦齐祖马布向治疗的并发症. 该工具通过评估患者的风险因素,帮助瘤学家个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 具有提高瘤学临床决策的潜力.
- 管理与贝瓦西祖马布等向治疗相关的风险对于患者的治疗结果至关重要.
研究的目的:
- 开发和验证基于机器学习的临床决策支持系统 (CDSS),用于预测贝瓦西祖马布或其生物类似药的并发症.
- 将预测模型转化为个性化癌症护理的临床适用工具.
主要方法:
- 一项前性观察性研究涉及395名接受贝瓦齐祖马布或生物仿制药治疗固体瘤的患者.
- 机器学习模型 (随机森林,逻辑回归,XGBoost) 通过使用治疗前变量进行训练和验证.
- 最好的模型提供了后勤风险评分和风险分层的交互式HTML表格.
主要成果:
- 优化的随机森林模型实现了70.63%的准确性和AUC-ROC为0.75.
- 衍生后勤风险得分显示出良好的表现 (AUC-ROC = 0.720) 并确定了关键预测因素,如年龄,贫血和瘤阶段.
- 该工具有效地将患者分为低,中,高风险并发症类别.
结论:
- 这项研究证明了使用真实世界的数据在瘤学中使用人工智能驱动的预测工具的可行性.
- 开发的风险评分和交互形式可以帮助临床医生量身定制有针对性的治疗决策,促进个性化瘤治疗.
- 该工具作为决策支持,补充而不是取代临床判断.
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