Intelligent diagnosis of Kawasaki disease from real-world data using interpretable machine learning models
This study used diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models such as EBM outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.
Authors
- Zhengyong Hu
- Mingkun Tang
- Haolin Wang (ORCID: https://orcid.org/0000-0002-1735-9525)
- Qing Qian (ORCID: https://orcid.org/0000-0002-4197-9919)
- Zhenli Chen (ORCID: https://orcid.org/0009-0008-9664-085X)
- Ruiqi Wang (ORCID: https://orcid.org/0000-0002-2095-160X)
- Haoran Chen (ORCID: https://orcid.org/0000-0003-2407-8518)
- Yifan Duan (ORCID: https://orcid.org/0000-0003-2323-8460)
- Zhilin Huang
- Wanfei Hu
- Jiayin Zhou (ORCID: https://orcid.org/0009-0002-3269-0723)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Hellenic Journal of Cardiology
- Published
- 2024-08-10
- DOI
- https://doi.org/10.1016/j.hjc.2024.08.003
- Citations
- 6
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Chongqing Medical University
- Chinese Academy of Medical Sciences
- National Key Research and Development Program of China
- Chinese Academy of Medical Sciences Initiative for Innovative Medicine