An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography
The weaning outcome prediction model based on EIT data does not rely on ventilator data, which is suitable for a broader range of weaning scenarios. We proposed a comprehensive ML framework for weaning outcome prediction and incorporated the SHAP and LIME methods, which significantly improved the interpretability of the model.
Authors
- Meng Dai (ORCID: https://orcid.org/0000-0003-4389-8888)
- Tao Chen (ORCID: https://orcid.org/0009-0005-6258-902X)
- Zhanqi Zhao (ORCID: https://orcid.org/0000-0002-1279-2207)
- Mei-Yun Chang
- Feng Fu (ORCID: https://orcid.org/0000-0002-4286-5227)
- Yifan Liu (ORCID: https://orcid.org/0000-0002-3378-8469)
- Pu Wang (ORCID: https://orcid.org/0000-0002-9617-5686)
- Yeong-Long Hsu
- Hai-Yen Hsia
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Yuanpei University (TW)
- Peking Union Medical College Hospital (CN)
- Far Eastern Memorial Hospital (TW)
- Tianjin Medical University (CN)
- Guangzhou Medical University (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108811
- Citations
- 1
- Primary Topic
- Context-Aware Activity Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.28