机器学习框架用于早期识别使用行政索赔的高支出克罗恩病患者
Lukasz S Wylezinski1, Jamieson D Gray1, Charles F Spurlock2,3,4,5
1Decode Health Inc, Nashville, Tennessee, USA.
BMJ health & care informatics
|February 27, 2026
概括
机器学习模型可以预测克罗恩病 (CD) 患者的高医疗保健支出. 这允许主动干预来管理成本和改善患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
- 胃肠病学 胃肠病学
背景情况:
- 克罗恩氏病 (CD) 与高医疗保健支出有关.
- 准确预测高成本患者对于资源配置和干预策略至关重要.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测医疗保健支出最高四分之一的CD患者.
- 与传统方法相比,评估ML模型提供的预测准确性和交付时间.
主要方法:
- 利用了大约267000名会员的非识别商业索赔数据 (2016-2018年),其中包括994起CD案件.
- 汇总的每月患者数据,包括支出,人口统计,诊断,程序和药物代码.
- 使用交叉验证调整了七个ML算法家族,并前性地测试了它们预测高成本支出的能力.
主要成果:
- 机器学习模型预测了最高四分之一成员80%的支出,显著超过了基线历史方法 (67%和62%).
- 与四个月的历史方法相比,每月识别的新高成本患者数量几乎是每月的两倍.
- 机器学习模型在预测住院患者接触方面表现出更高的准确性,导致过度成本.
结论:
- 基于索赔的ML模型为付款人和临床医生提供了宝贵的领先时间,以优化患者管理,并可能减少急诊.
- 这种ML框架使用标准索赔数据,可以适应预测高成本在其他条件有显著的支出变化.
- 未来的验证证实,ML模型增强了短期风险分层,用于识别高成本的CD患者.
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