AI Article Synopsis
- This study investigates the use of machine learning to predict aspirin resistance (AR) in Chinese stroke patients by examining patient characteristics and specific genetic mutations.
- The research analyzed 2405 patients and found significant mutation frequencies for GP1BA rs6065 and LTC4S rs730012, with 5.26% and 14.78% respectively.
- The study indicates that identifying rs6065 mutations could enhance personalized aspirin treatment plans for patients, while also revealing that certain machine learning models like random forest and XGBoost are effective in predicting AR.
Article Abstract
This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of and . 2405 patients were analyzed to measure the Mutation frequency of rs6065 and rs730012. 112 patients with first-stroke arteriostenosis were prospectively enrolled to establish machine learning model. GP1BA rs6065 mutation frequency is 5.26% and LTC4S rs730012 is 14.78%. rs6065 CT patients have more sensitivity to aspirin than CC genotype. Simple linear regression identified significant associations with age, smoking, HDL and rs6065. Random forest (RF) and extreme gradient boosting (XGBoost) demonstrated predictive capabilities for AR. Findings suggest pre-identifying rs6065 could optimize aspirin treatment, enabling personalized care and future research avenues.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/14622416.2024.2411939 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!