通过使用基于深度学习的自然语言处理来减少弹射率来自动识别心力衰竭.
Arash A Nargesi1, Philip Adejumo2, Lovedeep Singh Dhingra2
1Heart and Vascular Center, Brigham and Women's Hospital, Harvard School of Medicine, Boston, Massachusetts, USA.
JACC. Heart failure
|October 25, 2024
概括
一个新的深度学习模型从临床笔记中准确地识别了心力衰竭患者的减少喷射率 (HFrEF). 该工具自动化了HFrEF护理的质量评估,改善了患者的治疗结果.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 心脏病学研究 心脏病学研究
背景情况:
- 缺乏自动化工具来衡量心力衰竭与减少喷射率 (HFrEF) 的护理质量.
- 这种差距阻碍了在HFrEF中评估指导方针指导的护理的国家计划的实施.
研究的目的:
- 开发一种自动化方法,用于在医院出院时识别HFrEF患者.
- 为了及时评估和改善HFrEF患者的护理质量.
主要方法:
- 创建了一个深度学习语言模型,以从医院出院摘要中识别HFrEF.
- HFrEF被定义为左心室喷射率<40%通过心声学.
- 该模型使用来自耶鲁纽黑文医院,西北医学院,耶鲁社区医院和MIMIC-III数据库的数据进行了验证.
主要成果:
- 该模型在检测HFrEF方面取得了很高的性能,在持有装置上,AUROC为0.97和AUPRC为0.97.
- 外部验证在多个机构和数据集中显示出强的表现 (AUROC范围从0.91到0.95).
- 与诊断代码相比,模型预测显著改善了HFrEF重新分类 (60.2% ± 1.9%的改善,P < 0.001).
结论:
- 一个新的语言模型有效地从临床笔记中识别HFrEF,其精度和准确性很高.
- 这种自动化方法对于提高HFrEF护理质量评估至关重要.
- 开发的模型代表了利用临床笔记为质量改进计划的重大进步.
相关概念视频
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The first step in nursing management of a patient with heart failure involves thoroughly assessing the patient's medical history.Subjective Data: Obtain the patient's medical history of coronary artery disease, hypertension, myocardial infarction, and symptoms like dyspnea, orthopnea, and paroxysmal nocturnal dyspnea.Objective Data: Conduct a physical examination to identify findings such as jugular vein distention, pulmonary crackles, tachycardia, murmurs, peripheral edema, and vital signs,...
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01:27Heart Failure I: Introduction
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Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
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