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机器学习驱动的预测医院入院使用梯度增强和GPT-2的预测

Xingyu Zhang¹, Hairong Wang², Guan Yu³

¹Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.

|March 31, 2025

概括

预测急诊室 (ED) 的住院情况至关重要. 将结构化和非结构化数据与机器学习模型集成,可以显著提高预测准确性,提高患者护理和资源管理.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
临床决策支持系统临床决策支持系统

背景情况:

从急诊室 (ED) 准确预测住院情况对于优化患者护理和资源配置至关重要.
当前的预测方法通常依赖于有限的数据类型,可能会影响准确性.

研究的目的:

开发和评估机器学习模型,用于预测来自ED的入院病例.
评估整合结构化临床数据与非结构化文本数据对预测性能的影响.

主要方法:

利用了2021年国家医院门诊医疗保健调查-紧急部门 (NHAMCS-ED) 的数据.
采用渐变增强分类器用于结构化数据和微调的GPT-2模型用于非结构化文本 (首席投诉,伤害描述).
通过从两个单个模型中平均预测并使用5倍交叉验证进行评估,开发了一个组合模型.

主要成果:

组合模型的准确度达到75.8%,超过结构化数据模型 (73.8%) 和非结构化数据模型 (64.6%).
组合模型表现出卓越的性能,在接收器操作特征曲线 (AUC-ROC) 下的最高面积.
组合模型的灵敏度和特异性为75.8%.

结论:

整合结构化和非结构化数据与机器学习模型显著提高了预测医院入院的ED.
这种混合方法为改善临床决策和优化急诊室操作提供了一个有希望的策略.

关键词:

医院入院预测预测紧急情况部门的急救部门.机器学习是机器学习.自然语言处理自然语言处理.

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