LivXAI-Net:一种可解释的人工智能框架用于肝病诊断,基于物联网的实时监控支持
Deepak Kumar1, Brijesh Bakariya1, Chaman Verma2
1Department of Computer Application, IKGPTU, Jalandhar, 144603, Punjab, India.
Computer methods and programs in biomedicine
|July 22, 2025
概括
这项研究介绍了LivXAI-Net,这是一个可解释的AI系统,使用物联网生物传感器进行早期肝脏疾病检测. 它提供实时风险预测,以改善主动性肝脏健康管理.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 肝病是一个重大的全球健康挑战,由于诊断局限性,经常被发现迟到.
- 早期发现肝病对于有效管理和预防肝硬化等严重后果至关重要.
- 目前的诊断方法在及时和持续监测肝脏健康方面存在局限性.
研究的目的:
- 开发和评估LivXAI-Net,这是一个可解释的人工智能 (XAI) 框架,与物联网 (IoT) 生物传感器集成.
- 模拟实时数据采集和处理,用于主动性肝病风险评估.
- 提高人工智能模型在肝病诊断中的可解释性.
主要方法:
- 使用历史数据集 (Mayo Clinic PBC,n=424) 来训练随机森林 (RF) 和XGBoost (XGB) 机器学习模型.
- 集成的可穿戴生物传感器 (汗水,血小板,前) 并使用SHAP/PFI进行模型解释.
- 开发了一个安全的移动应用程序 (肝脏健康跟踪器),具有实时风险预测功能.
主要成果:
- 射频和XGB模型通过20倍交叉验证实现了高精度 (84%和82%).
- 通过SHAP分析,关键生物标志物 (白蛋白,胆固醇,甘油三) 被确定为重要的预测因素.
- 该系统在模拟的5G环境中演示了低延迟 (0.85s),用于快速报警.
结论:
- LivXAI-Net有效地将可解释的ML与实时生物传感器数据相结合,用于主动管理肝病.
- 该框架显示了早期检测和肝脏健康的持续监测的前景.
- 未来的研究将专注于现实世界的临床验证与实时传感器数据.
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