基于回顾性数据和ADOS-2评分的自闭症谱系障碍和多系统发育障碍诊断的机器学习方法
Marilena Briguglio1, Laura Turriziani1, Arianna Currò1
1Unit of Child Neurology and Psychiatry, Department of Human Pathology of the Adult and Developmental Age "Gaetano Barresi", Polyclinic Hospital University, 98125 Messina, Italy.
Brain sciences
|June 28, 2023
概括
机器学习模型确定了自闭症谱系障碍 (ASD) 和多系统发育障碍 (MSDD) 的关键预测因素. 肠道干扰,睡眠问题和EEG发现显著影响ASD诊断,有助于早期干预.
科学领域:
- 神经科学是一个神经科学.
- 发育儿科 发育儿科
- 计算生物学 计算生物学
背景情况:
- 早期诊断自闭症谱系障碍 (ASD) 对于有效干预和改善结果至关重要.
- 自闭症诊断观察表第二版 (ADOS-2) 等标准化评估的潜在假阳性包括在内.
- 将ASD与其他早期发育状况区分开来,例如多系统发育障碍 (MSDD),需要精确识别预测因素.
研究的目的:
- 将机器学习模型应用于回顾性数据,以确定ASD和MSDD的最佳预测因素.
- 为了确定ADOS-2和相关的临床数据中哪些特定项目最能描述这些疾病.
- 提高诊断准确度,为幼儿提供量身定制的治疗策略.
主要方法:
- 对被诊断患有ASD和MSDD的患者的孕产妇,婴儿和ADOS-2数据的回顾性分析.
- 实施各种机器学习模型,包括RIDGE,线性回归,LASSO和CART分类和回归.
- 基于正在估计的特定结果 (例如,ADOS社会影响得分,重复行为得分,总得分或诊断分类) 评估模型性能.
主要成果:
- 里奇回归确定了肠道障碍,EEG检索和睡眠问题作为ADOS社会影响评分的关键预测因素.
- 线性回归表明,妊娠期,精神运动发育和肠道障碍最好地预测了ADOS重复性和受限制行为得分.
- 拉索回归突出了EEG检索,睡眠障碍,诊断时的年龄,妊娠期,出生体重,肠道障碍和神经学发现作为ADOS总体得分的重要预测因素.
- 卡特模型表明,诊断时的年龄和出生体重在区分ASD和MSDD方面最有效.
结论:
- 机器学习有效地识别了区分ASD和MSDD的关键早期指标.
- 临床因素,如肠道障碍,睡眠模式,神经学发现和出生参数是重要的预测因素.
- 这些发现可以完善诊断工具,并支持开发针对早期发育障碍的有针对性的干预措施.
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