通过深森林应用到电子健康记录来改善诊断
Atieh Khodadadi1, Nima Ghanbari Bousejin2, Soheila Molaei3
1Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
一个新的模型 - - 患者森林 (Patient Forest) 使用电子健康记录数据准确预测患者的再入院率和死亡率. 它甚至在有限的数据上也表现出色,优于其他机器学习方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 是复杂的,高维的数据来源,对患者护理至关重要.
- 识别EHR数据中的隐性相关性可以增强治疗和管理策略.
- 现有的模型在建立不同医学概念之间的稳定关系方面存在局限性.
研究的目的:
- 引入患者森林,这是一个新的端到端方法,用于从树结构的EHR数据中学习患者表示.
- 开发一个准确可靠的分类器,用于预测患者再入院和死亡率.
- 为了应对有限的数据源在EHR分析中的挑战.
主要方法:
- 开发了患者森林,这是一个端到端的模型,利用树结构数据的统计特征.
- 应用该模型来预测患者再入院和死亡率.
- 在MIMIC-III和eICU数据集上进行实验.
- 使用t-SNE进行了定性评估,以可视化学习的表示.
主要成果:
- 与现有的机器学习模型相比,患者森林在预测再入院和死亡率方面表现优越.
- 当培训数据有限时,该模型表现出特别高的效率.
- t-SNE可视化证实了模型学习有意义的EHR表示的能力.
结论:
- 患者森林提供了一种有效的方法,可以从EHR数据中学习患者的表征.
- 该模型提供准确可靠的预测再接收和死亡率,特别是在数据稀缺的情况下.
- 这种方法提高了EHR数据对临床决策支持和研究的有用性.
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