混合人工智能框架用于早期检测心力衰竭:将传统的机器学习和生成语言模型与临床数据集成
Abedalrahman Alshraideh1, Bayan Al Fayoumi2, Bahaaldeen M Alshraideh3
1General Internal Medicine, East Midlands Deanery - NHS England, Nottingham, GBR.
Cureus
|July 10, 2025
概括
结合CNN和LLM的新混合人工智能 (AI) 模型准确预测心力衰竭 (HF) 准确率为95.1%. 这种人工智能方法增强了心血管疾病诊断,改善了患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 早期检测和风险分层对于管理心血管疾病,特别是心力衰竭 (HF) 至关重要.
- 现有的诊断方法需要改进,以提高准确性和效率.
研究的目的:
- 开发和评估一种混合人工智能模型,用于增强心力衰竭预测.
- 整合卷积神经网络 (CNN) 和大型语言模型 (LLM) 进行全面的患者数据分析.
- 评估模型的准确性,可解释性和临床相关性.
主要方法:
- 开发了一个混合AI模型,集成结构化数据的CNN和非结构化临床信息的LLM.
- 该模型使用患者健康记录进行了训练和验证.
- 为了实现模型透明度,使用了包括SHAP在内的可解释AI (XAI) 技术.
主要成果:
- 混合人工智能模型实现了95.1%的预测准确度.
- 性能指标包括高精度,回忆,F1得分和AUC-ROC,超过独立模型.
- 确定HF的关键预测因素包括胸痛类型,最大心率 (maxHR) 和运动诱导的胸痛.
结论:
- 混合人工智能模型为准确和可解释的心血管诊断提供了一个有希望的方法.
- 开发的模型可以显著帮助医疗保健专业人员在早期心力衰竭检测和风险分层.
- 将人工智能整合到心血管医学中有可能改善患者的治疗结果,并支持临床决策.
关键词:
心血管疾病心血管疾病临床决策支持 临床决策支持卷积神经网络 (cnn) 是一个卷积神经网络.深度学习是一种深度学习.可以解释的AI心脏衰竭是因为心脏衰竭.混合AI模型的混合AI模型.大型语言模型 (llm)预测 预测 预测 预测更多相关视频
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Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
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01:29Heart Failure II: Pathophysiology
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Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
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