建立基于液态神经网络的急性胰腺炎严重性的预测模型
Jie Cao1, Shike Long2, Huan Liu1
1Department of Gastroenterology, The Second Affiliated Hospital of Guilin Medical University, Guilin, 541199, China.
Scientific reports
|May 13, 2025
概括
一个新的液体神经网络 (LNN) 模型准确地预测患者的严重急性胰腺炎 (SAP). 这种人工智能方法识别了关键指标,改善了急性胰腺炎的早期诊断和患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 严重的急性胰腺炎 (SAP) 具有显著的发病率和死亡率风险.
- 早期准确预测SAP严重程度对于有效的患者管理至关重要.
- 当前的诊断方法可能缺乏及时干预所需的精度.
研究的目的:
- 开发和评估一种新的液态神经网络 (LNN) 模型,用于预测SAP严重性.
- 将LNN模型的性能与传统机器学习模型进行比较.
- 使用可解释的人工智能技术识别SAP严重性的关键临床预测因素.
主要方法:
- 从2020年1月到2024年6月的急性胰腺炎 (AP) 患者数据的回顾性分析.
- 数据预处理和合成少数超样本技术 (SMOTE) 对失衡.
- 后勤回归,决策树,随机森林,XGBoost和LNN模型的开发和比较.
- 使用ROC曲线下的面积 (AUC) 和其他指标进行绩效评估.
- 为了模型的可解释性,夏普利添加式解释 (SHAP).
主要成果:
- 在预测AP严重程度方面,LNN模型取得了最高的表现,AUC为0.9659.
- 与其他模型相比,LNN显示出更高的准确性,精度,回忆,F1得分和特异性 (>0.90).
- SHAP分析确定了水平,氨酶活性和基基的百分比作为SAP的显著预测因素.
结论:
- 液态神经网络 (LNN) 模型显示了早期评估AP严重性的特殊潜力.
- 在管理AP患者时,LNN提供了一个有前途的临床决策支持工具.
- 改善早期严重性预测可以导致优化治疗策略和更好的患者预后.
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