基于性别的性别差异在产前和产周预测自闭症谱系障碍的预测者使用机器学习与国家卫生数据
Ju Sun Heo1,2, Seung-Woo Yang3,4, Sohee Lee5,6,7
1Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea.
概括
性别特定的因素,如孕期体重指数 (BMI) 和社会经济地位显著预测自闭症谱系障碍 (ASD) 风险. 机器学习模型在大型人口研究中准确地识别了这些预测因素.
科学领域:
- 神经发育障碍 神经发育障碍
- 遗传学和表观遗传学
- 环境健康 环境健康
背景情况:
- 自闭症谱系障碍 (ASD) 影响男性比女性更多,这表明性别特异性风险因素.
- 了解这些因素对于早期识别和干预至关重要.
- 以前的研究表明,遗传,表观遗传和环境影响的复杂相互作用.
研究的目的:
- 开发特定于性别的机器学习模型来预测自闭症风险.
- 确定ASD的关键产前和产周预测因素.
- 分析风险因素与自闭症之间的性别相关关系.
主要方法:
- 使用韩国国家医疗保险服务索赔数据库进行回顾性队列研究.
- 包括75,105个单独生育的婴儿及其母亲,以及随访数据.
- 应用随机森林模型与夏普利添加式扩展 (SHAP) 进行预测分析.
主要成果:
- 随机森林模型显示出高准确度 (0.996) 和ASD预测的AUC (0.997).
- 关键预测因素包括妊娠前的BMI,社会经济地位,产妇年龄,性别和分娩机构.
- SHAP分析揭示了性别特异性模式:低BMI增加了两性风险,女性高BMI,U形社会经济地位风险.
结论:
- 针对自闭症的性别特定预测模型是可行的和准确的.
- 孕期BMI和社会经济地位是ASD风险的关键性,性别依赖的预测因素.
- 这些发现强调了在产前和产周护理中需要量身定制的风险评估策略.
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