使用可解释机器学习方法确定老年霍奇金淋巴瘤幸存者的医疗保健成本驱动因素
Zasim Azhar Siddiqui1, Yves Paul Mbous1, Sabina Nduaguba1
1Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown.
Journal of managed care & specialty pharmacy
|March 28, 2025
概括
机器学习确定了霍奇金淋巴瘤 (HL) 的老年人医疗保健成本的关键驱动因素. 基线支出,药物和心脏问题预测了预诊成本,而治疗则推动了以后的支出.
科学领域:
- 在瘤学瘤学.
- 卫生经济学 卫生经济学
- 数据科学数据科学数据科学
背景情况:
- 霍奇金淋巴瘤 (HL) 的医疗保健支出正在上升,特别是在老年人中.
- 关于老年HL患者医疗保健支出驱动因素的文献有限.
- 机器学习 (ML) 提供了一种数据驱动的方法来识别支出预测因素.
研究的目的:
- 确定老年HL幸存者的医疗保健支出的主要预测因素.
- 分析诊断前,治疗前和治疗后阶段的预测因素.
- 利用ML来了解老年HL患者群体的成本驱动因素.
主要方法:
- 对监测,流行病学和最终结果-医疗保险数据 (2009-2017年) 的回顾性分析.
- 分析了三个癌症治疗阶段 (诊断前,治疗后,治疗后),包括12个月的基线和随访期.
- 用于预测的XGBoost,随机森林和线性回归;用于预测器识别的SHapley添加式扩展 (SHAP).
主要成果:
- XGBoost回归显示了医疗保险支出的强有力的预测性表现 (R^2高达0.46).
- 前诊断阶段:基线支出,处方数和心律失常是关键预测因素.
- 治疗阶段:化疗和免疫治疗;治疗后阶段:手术和免疫治疗预计成本.
结论:
- 机器学习模型可以有效地预测HL护理的不同阶段的医疗保健支出.
- 确定特定阶段的成本预测因素对于金融困难缓解策略至关重要.
- 调查结果可以为政策制定提供信息,以支持HL幸存者.
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