Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan
Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan
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DOI：
10.1177/1087054717740632
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发表时间：
2020-12-01
影响因子：
3
通讯作者：
Inagaki, Masumi
中科院分区：
文献类型：
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作者：
Yasumura, Akira;Omori, Mikimasa;Inagaki, Masumi
目的：利用机器学习建立有效、客观的ADHD生物标志物。方法：采用支持向量机（SVM），利用机器学习从新的脑功能数据中预测障碍严重程度。使用多中心方法收集用于机器学习训练的数据，包括来自108名ADHD儿童和108名正常发育（TD）儿童的行为和生理指标，年龄和反向Stroop任务（RSTT）数据。近红外光谱（NIRS）被用来量化的变化，前额皮质氧合血红蛋白在睡眠。结果：支持向量机的总体性能结果显示，其敏感性为88.71%，特异性为83.78%，总判别率为86.25%。
Objective:To establish valid, objective biomarkers for ADHD using machine learning.Method:Machine learning was used to predict disorder severity from new brain function data, using a support vector machine (SVM). A multicenter approach was used to collect data for machine learning training, including behavioral and physiological indicators, age, and reverse Stroop task (RST) data from 108 children with ADHD and 108 typically developing (TD) children. Near-infrared spectroscopy (NIRS) was used to quantify change in prefrontal cortex oxygenated hemoglobin during RST. Verification data were from 62 children with ADHD and 37 TD children from six facilities in Japan.Results:The SVM general performance results showed sensitivity of 88.71%, specificity of 83.78%, and an overall discrimination rate of 86.25%.Conclusion:A SVM using an objective index from RST may be useful as an auxiliary biomarker for diagnosis for children with ADHD.