模拟使用深度神经网络的高血压专家的治疗经验
Jong-Chol Ri1, Kum-Ryong Jo2, Tae-Ok Mun2
1Faculty of Information Science, Kim Il Sung University, Pyongyang, Democratic People's Republic of Korea.
Journal of clinical hypertension (Greenwich, Conn.)
|October 30, 2025
概括
一个新的深度学习模型准确地模拟高血压专家的药物选择,并预测患者的血压 (BP) 和心率 (HR) 反应. 这种人工智能方法显示出对标准化高血压护理和推进精确医学的承诺.
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:
- 高血压管理涉及复杂的医生决策,平衡药物疗效和患者因素,通常依赖于难以量化的经验.
- 现有的临床指南可能无法完全捕捉高血压专家的细微决策过程.
- 量化和复制专家专业知识对于提高治疗标准化和精度至关重要.
研究的目的:
- 开发和验证一个模拟高血压专家处方模式的深度学习模型.
- 预测随后对处方药物的生理反应 (血压和心率).
- 评估模型在可接受的范围内复制临床决策的能力.
主要方法:
- 设计了一个双块深度神经网络 (DNN) 框架,一个块用于处方预测,另一个块用于生理反应预测.
- 该模型同时使用临床试验数据的多目标方法进行训练,使用Huber损失函数来实现稳定性.
- 使用平均绝对误差 (MAE),误差差异和平均相对误差 (MRE) 来评估性能.
主要成果:
- 该DNN模型实现了药物后血压的高预测精度,误差始终低于10 mmHg (MAE = 6.2 ± 1.8 mmHg).
- 药物剂量预测与实际专家处方有很强的一致性,平均相对误差为0.12%.
- 该模型有效地在临床上可接受的参数内复制了医生决策.
结论:
- 开发的深度学习框架成功模拟高血压专家的治疗策略,并预测患者的生理结果.
- 这种人工智能驱动的方法有可能使护理标准化,减少决策的变化,并提高高血压管理中的精准医学.
- 该研究作为概念验证,突出了用于临床决策支持的双块DNN架构的可行性,并建议在未来对更大,多中心数据集进行验证.
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