关于人工智能驱动的方法来分类心理健康障碍的试点研究
Naman Dhariwal1, Nidhi Sengupta1, M Madiajagan1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Frontiers in human neuroscience
|April 25, 2024
概括
人工智能通过分析生活方式和环境因素,准确地预测心理健康障碍. 这种人工智能方法有助于医疗保健专业人员和政策制定者解决这些疾病的全球流行率不断上升的问题.
科学领域:
- 计算神经科学是一种计算神经科学.
- 公共卫生信息学 公共卫生信息学
- 环境流行病学环境流行病学
背景情况:
- 全球精神疾病患病率不断上升,特别是在年轻人中.
- 需要先进的分析方法来了解有助于因素.
- 现有的数据集,如城市健康倡议,为预测建模提供了潜力.
研究的目的:
- 开发和验证基于人工智能 (AI) 的方法来分析精神障碍的患病率.
- 为了将生活方式 (吸烟,酒精) 和环境 (空气/噪音污染) 因素与心理健康结果相关联.
- 利用机器学习和深度学习进行准确的预测和趋势预测.
主要方法:
- 分析城市健康倡议数据集.
- 机器学习和深度学习模型的应用与超参数调整.
- 数据科学,统计,优化和数学建模的整合.
主要成果:
- 卷积神经网络 (CNN) 在预测心理健康事件方面取得了99.79%的准确性.
- XGBoost模型显示了95.30%的准确性和0.9985.5的ROC曲线.
- 斯特鲁普测试的表现是最重要的预测因素 (重要性得分:0.135),其次是吸烟和饮酒.
结论:
- 人工智能驱动的预测模型可以准确预测精神障碍趋势.
- 这项研究强调了生活方式和环境因素对心理健康的重大影响.
- 研究结果可以支持医疗保健专业人员进行早期诊断和治疗,并为有关成和污染控制的政策决策提供信息.
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