为个性化注意力缺陷多动症 (ADHD) 治疗设计一个协作知识框架
Pornsiri Chatpreecha1, Sasiporn Usanavasin1
1School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand.
一个新的框架有助于诊断注意力缺陷多动症 (ADHD) 和个性化治疗. 机器学习,特别是决策树算法,在分类多动症类型方面实现了99.60%的准确性.
科学领域:
- 神经发育障碍 神经发育障碍
- 儿科行为健康儿童行为健康
- 医疗保健中的人工智能
背景情况:
- 注意缺陷多动性障碍 (ADHD) 是一种流行的神经发育障碍,影响了超过一百万泰国年轻人 (2012-2018).
- 未经治疗的ADHD会给个体带来长期的挑战.
- 男性被诊断出患有多动症的频率高于女性.
研究的目的:
- 为个性化ADHD治疗建议设计一个协作知识框架.
- 开发一种查工具,用于观察和记录儿童的ADHD行为症状.
- 实施算法来分类ADHD类型并推量身定制的治疗方法.
主要方法:
- 开发了一个查工具,结合了医生验证的标准和范德比尔特ADHD标准化查工具.
- 四个机器学习算法 (决策树,天真贝叶斯,神经网络,k-最近邻居) 用于ADHD类型分类.
- 算法的性能被评估为准确性和F1分数.
主要成果:
- 决策树算法表现出最高的平均准确率99.60%.
- 所有测试的机器学习算法在分类ADHD类型方面都获得了97%或更高的F1分数.
- 开发的查工具有助于医生,家长和教师共同观察症状.
结论:
- 一个新的框架和查工具可以提高ADHD的诊断和管理.
- 机器学习算法,特别是决策树,对于准确的ADHD分类非常有效.
- 可以根据分类的ADHD类型推个性化行为疗法和活动.
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