通过人工神经网络对精神分裂症病例进行分类,使用日本基于网络的调查数据:病例对照研究
Yupeng He1, Masaaki Matsunaga1, Yuanying Li2
1Department of Public Health, Fujita Health University School of Medicine, Toyoake, Japan.
JMIR formative research
|November 15, 2023
概括
这项研究开发了一个人工神经网络 (ANN) 模型,使用日本网络调查数据准确地分类精神分裂症病例. 该ANN模型表现出高性能,有助于精确估计精神分裂症的流行率.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 精神病学是一个精神病学.
背景情况:
- 估计日本的精神分裂症患病率是具有挑战性的.
- 精神分裂症患者经常经历并发症和健康状况不佳.
- 机器学习 (ML) 为流行病学研究提供高精度建模.
研究的目的:
- 为准确的精神分裂症病例分类构建一个人工神经网络 (ANN) 模型.
- 使用日本网络调查数据验证ANN模型的概括性.
主要方法:
- 利用一个大型的日本基于网络的调查数据集 (2021年),其中包括223例精神分裂症病例和1776例对照.
- 使用ANN进行分类,以后勤回归 (LR) 作为参考.
- 使用诸如接收器运行特征曲线下的面积,精度和特异性等指标比较模型性能.
主要成果:
- 在曲线下的面积 (0.86比0.78) 和特异性 (0.96比0.94) 方面,ANN模型的表现优于LR.
- LR显示了更高的灵敏度 (0.63比0.56).
- 关键的预测变量包括睡眠药物的使用,年龄,收入和就业类型.
结论:
- 一个ANN模型成功地被开发出来,用于从网络调查数据中分类精神分裂症病例.
- 该模型显示了高的内部有效性.
- 这些发现支持改善日本精神分裂症的流行率估计和未来的流行病学研究.
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