通过人工智能对心脏衰竭患者按性别的心电图参数进行比较,这些患者有保存的喷射部分
1Department of Cardiology, Faculty of Medicine, Samsun University, Samsun 33805, Turkey.
Diagnostics (Basel, Switzerland)
|October 28, 2023
概括
人工智能识别了关键的心电图 (ECG) 标志物,以区分男性和女性心力衰竭患者与保存的喷射分数 (HFpEF). 这有助于HFpEF的早期诊断和定制治疗.
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) 的心力衰竭越来越普遍,并且与高住院率有关.
- 早期诊断和治疗对于改善HFpEF患者的预后至关重要.
- 有限的研究存在于HFpEF的性别特异性心电图 (ECG) 变化.
研究的目的:
- 为了比较HFpEF患者的性别特异性心电图参数.
- 利用人工智能 (AI) 和机器学习来识别男性和女性HFpEF患者之间的区分ECG特征.
主要方法:
- 对118名HFpEF患者 (66名女性,52名男性) 的人口,心声和心电图特征的分析.
- 应用人工智能和机器学习算法 (梯度提升,k-NN,后勤回归,随机森林,SVM) 来区分性别.
- 确定影响区分的关键参数.
主要成果:
- 随机森林模型在区分男性和女性HFpEF患者方面实现了84.7%的准确性.
- 重要参数包括吸烟状态,P波分散,P波振幅,T端P/(PQ*年龄),康奈尔产品和P波持续时间.
- 这些心电图标记提供了对高高压pEF的性别差异的见解.
结论:
- 对心电图参数的AI驱动分析为医生提供了宝贵的工具.
- 促进性别特异性HFpEF病例的诊断,治疗和跟踪.
- 利用可访问的心电图数据可以提高患者的护理和决策.
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