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Updated: Jul 7, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

人工智能方法用于更好地检测未被诊断的心力衰竭,使用保存的射出分数

Jack Wu¹, Dhruva Biswas^1,2, Matthew Ryan^1,2

¹School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK.

European journal of heart failure

|December 28, 2023

概括

未被诊断的心力衰竭与保存的喷射分数 (HFpEF) 构成显著的死亡风险. 自然语言处理 (NLP) 可以在电子健康记录 (EHR) 中识别这些有风险的患者,以便及时进行临床审查.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

保存喷射分数 (HFpEF) 的心力衰竭被低诊断,占所有心力衰竭 (HF) 病例的近一半.
准确的HFpEF诊断对于有效的患者管理和治疗至关重要.
欧洲心脏病学会 (ESC) 的标准存在,但在常规临床实践中未得到充分利用.

研究的目的:

探索自然语言处理 (NLP) 在识别未诊断的HFpEF患者使用电子健康记录 (EHR) 的实用性.
根据ESC标准评估未诊断的HFpEF的流行情况.
评估符合ESC标准的HFpEF患者的临床结果,与正式诊断的患者相比.

主要方法:

一个回顾性队列研究,利用NLP管道对2010-2022年EHR数据进行研究.
鉴定HF患者,然后根据左心室喷射率 (LVEF) 进行分类.
在一个独立的中心验证NLP识别的HFpEF病例.

主要成果:

在3727名LVEF≥50%的高压患者中,只有8.3%的患者被临床医生诊断为HFpEF.
显著的75.4%符合ESC对HFpEF的标准,但缺乏正式诊断.
符合ESC标准的患者,尽管并发症较少,但与被诊断为HFpEF患者相比,5年死亡率更高.

结论:

未被诊断的HFpEF患者代表一个高风险群体,死亡率很高.
在EHR数据中,NLP是检测潜在的HFpEF病例的可行工具.
NLP可以促进更早的临床审查,并提高HFpEF的诊断准确性.

关键词:

人工智能的人工智能是人工智能.心力衰竭与保存的喷射分数的心脏衰竭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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