生活质量预测器的成本效益,以指导乳腺癌的心理社会支持
Tuukka Hakkarainen1,2, Ira Haavisto1, Mikko Nuutinen1
1Nordic Healthcare Group, 02150 Helsinki, Finland.
Cancers
|February 13, 2026
概括
整合机器学习生活质量 (QoL) 预测器与临床医生的判断提供了最具成本效益的策略,以针对乳腺癌患者的心理社会支持,改善结果.
科学领域:
- 在瘤学瘤学.
- 卫生经济学 卫生经济学
- 医疗保健中的人工智能
背景情况:
- 乳腺癌患者经常经历心理困扰,影响他们的生活质量 (QoL).
- 识别患有低QOL风险的患者以指导心理社会支持是临床上具有挑战性的.
- 机器学习 (ML) 为医疗保健环境中的预测建模提供了潜力.
研究的目的:
- 评估基于ML的QoL预测器的成本效益,以优化乳腺癌患者的心理社会支持决策.
- 为了比较单独使用ML预测器,单独使用临床医生的判断或两者的组合的经济价值.
主要方法:
- 采用决策树成本效益模型,时间视野为一年.
- 他们比较了四种策略:仅临床医生,仅预测者,临床医生加预测者和没有支持.
- 计算了经质量调整的寿命年 (QALY),成本和净货币收益 (NMB).
主要成果:
- 联合临床医生和ML预测器策略产生了最高的NMB (16,349欧元) 和QALY增长 (0.759).
- 这种综合方法的增量成本效益比率 (ICER) 为22,892.892欧元.
- 概率敏感性分析表明,在30,000欧元的支付意愿门上,成本效益的概率为69%.
结论:
- 将临床医生的专业知识与基于ML的QoL预测器相结合,是针对性心理社会支持的最具成本效益的策略.
- 这种综合方法提高了为乳腺癌患者提供支持的准确性.
- 建议进行进一步的研究,以验证长期临床有效性和成本效益.
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