Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients
This study indicates that machine learning methods hold potential for early prediction of mortality risk within 28 days of admission in patients with pulmonary fibrosis. Moreover, SHAP analysis enhanced the interpretability of the LightGBM model, thereby guiding clinical decision-making.
Authors
- Zijun Wu (ORCID: https://orcid.org/0000-0002-0671-0156)
- Gang Liu (ORCID: https://orcid.org/0000-0002-6426-3155)
- Zhiliang Xu (ORCID: https://orcid.org/0000-0003-3466-1803)
- Mingliang Li (ORCID: https://orcid.org/0009-0005-2797-3077)
Institutions
- Guangdong Medical College (CN)
- Affiliated Hospital of Guangdong Medical College Hospital (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-10-14
- DOI
- https://doi.org/10.1186/s12911-025-03172-8
- Primary Topic
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China