在经过导管切除的心房的患者中,特征性症状集群
Mollie Hobensack1, Yihong Zhao1, Danielle Scharp1
1Columbia University School of Nursing, New York City, New York, USA.
Open heart
|August 4, 2023
概括
自然语言处理和机器学习在接受移除的心房动 (AF) 患者中发现了六个不同的症状集群. 这些群体,包括呼吸障碍和,显示出不同的临床和人口统计联系,影响患者的护理.
科学领域:
- 医疗保健中的计算语言学和数据科学.
- 分析患者报告的结果和电子健康记录.
背景情况:
- 前庭动 (AF) 管理涉及导管切除,但患者的症状经历复杂多样.
- 了解症状模式可以改善临床护理和预测结果.
研究的目的:
- 在经过导管切除的心房动 (AF) 患者中识别同时发生的症状集群.
- 描述与这些已识别的症状集群相关的临床和社会人口学因素.
主要方法:
- 从1293名成年AF患者 (2010-2020年) 的电子健康记录 (EHR) 的回顾性分析.
- 自然语言处理 (NLP) 从临床笔记中提取了10个AF症状.
- 沃德的等级聚合集群识别了基于症状模式的患者子组.
主要成果:
- 确定了六个不同的症状集群,包括"一般症状"",呼吸不良和水"和"无症状".
- 最常见的症状是呼吸不良 (64%), (62%) 和心 (57%).
- 无症状集群与男性,白人患者和心力衰竭 (HF) 患者的患病率更高有关.
结论:
- 在AF患者中,NLP和机器学习有效地识别了不同的症状集群,表明潜在的潜在生物学差异.
- 这些症状集群对个性化临床管理有影响,需要进一步调查它们与剥离后结果的关联.
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