在患有炎症性肠病的患者中,基于机器学习的纵向医疗保健利用的表征
Berkeley N Limketkai1, Laura Maas2, Mahesh Krishna2
1Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA.
机器学习模型可以识别炎症性肠病 (IBD) 的高医疗保健资源用户. 这些模型准确地预测了未来的使用情况,有助于更好地分配资源和协调患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床操作 临床操作
背景情况:
- 炎症性肠病 (IBD) 与高医疗利用率有关.
- 预测高资源用户可以优化资源配置.
- 开发模型来预测患者需求对于高效的医疗保健管理至关重要.
研究的目的:
- 开发机器学习模型,根据临床利用模式对IBD患者进行分类.
- 根据基线临床数据预测纵向医疗保健利用率.
- 加强IBD护理中的资源配置和患者风险评估.
主要方法:
- 在两个学术中心对1174名IBD成年患者 (2015-2021) 的回顾性研究.
- 利用机器学习进行患者聚类和预测医疗保健利用情况.
- 将模型性能与顺序回归和随机选择方法进行比较.
主要成果:
- 聚类确定了不同的低,中,高资源利用群体.
- 机器学习模型预测了81%-85%的准确度 (AUC 0.84-0.90) 的纵向医疗保健利用率.
- 与传统方法相比,模型表现出优越的预测性能.
结论:
- 机器学习通过资源利用有效地将IBD患者聚集在一起.
- 使用基线临床因素,可以准确预测纵向利用率.
- 整合到电子健康记录中可以支持风险评估,护理协调和资源分配.
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