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PSO-BiLSTM-注意:一个可解释的深度学习模型,通过粒子群优化进行优化,以准确预测缺血性心脏病发病率
Ruihang Zhang1, Shiyao Wang2, Wei Sun1
1Graduate School, China Academy of Chinese Medical Sciences, Beijing 100102, China.
Bioengineering (Basel, Switzerland)
|December 30, 2025
概括
本研究引入了一个可解释的框架,用于预测缺血性心脏病 (IHD) 发病率,提高准确性并为公共卫生战略提供明确的见解.
科学领域:
- 流行病学 流行病学
- 心血管疾病研究研究
- 医疗保健中的人工智能
背景情况:
- 缺血性心脏病 (IHD) 是全球主要的死亡原因,需要精确的发病率预测才能有效预防.
- 当前的统计和深度学习模型在捕捉非线性模式和临床解释性方面存在局限性.
研究的目的:
- 为准确的IHD发病率预测开发一个可解释的预测框架.
- 提高流行病学预测中的深度学习模型的临床可解释性.
主要方法:
- 组合的粒子群集优化 (PSO),双向长期短期记忆 (BiLSTM) 网络,以及多层次的注意力机制.
- 使用的2021年全球疾病负担 (GBD) 数据 (1990-2021) 针对24个性别年龄子组的年龄标准化发病率 (ASIR).
- 采用了SHapley添加式解释 (SHAP) 来进行三级可解释性分析 (全球,本地,组件).
主要成果:
- 在MAE为0.0164,RMSE为0.0206和R2为0.97.9的R2下取得了优异的性能.
- 显著改善:93.96%的MAE减少与ARIMA相比,75.99%与CNN-BiLSTM相比.
- SHAP将60-64岁的女性和85-89岁的男性确定为主要预测组;残余连接捕获主要趋势 (71.0%),BiLSTM-Attention捕获非线性模式 (29.0%).
结论:
- 可解释的框架为公共卫生政策和资源分配提供了透明和精确的流行病学证据.
- 通过澄清预测因素,为高风险人群提供有针对性的干预策略.
- 将复杂的AI模型转化为临床和公共卫生决策的可理解工具.
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