Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients
ML proves to be a reliable tool for predicting LAT/SEC risk in NVAF patients. The constructed logistic regression model, along with SHAP interpretation, may serve as a clinically useful tool for identifying high-risk NVAF patients. This enables targeted diagnostic evaluations and the development of personalized treatment strategies based on the findings.
Authors
- Chaoqun Huang (ORCID: https://orcid.org/0000-0001-8015-6467)
- Shuyan Li (ORCID: https://orcid.org/0009-0007-0920-0956)
- Miaomiao Zhou (ORCID: https://orcid.org/0000-0002-5609-2341)
- Shangzhi Shu
- Zhenming Sun
Institutions
- First Bethune Hospital of Jilin University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1371/journal.pone.0313562
- Citations
- 17
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 23.60
Funders
- Innovative Research Group Project of the National Natural Science Foundation of China
- National Natural Science Foundation of China