使用深度学习预测精神分裂症的发病频率
Stephanie Yang1, Chih-Hsien Wu2, Li-Yeh Chuang3
1Department of Psychology, National Cheng-Kung University, Tainan City 701, Taiwan.
Asian journal of psychiatry
|September 7, 2024
概括
准确预测精神障碍如精神分裂症至关重要. 长期短期记忆 (LSTM) 模型显示,与其他方法相比,台湾住院患者的精神分裂症发病率的预测准确度更高.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 数据科学数据科学数据科学
背景情况:
- 全球精神疾病的流行率不断上升,需要准确的发病率预测.
- 有效的心理健康策略取决于可靠地预测精神障碍发病率.
研究的目的:
- 开发和评估一个长期短期记忆 (LSTM) 循环神经网络模型,用于预测台湾住院患者的精神分裂症发病率.
- 将LSTM模型的预测性能与其他统计和机器学习模型进行比较.
主要方法:
- 使用长期短期记忆 (LSTM) 循环神经网络模型.
- 来自台湾国家健康保险研究数据库 (NHIRD) 的数据,用于被诊断为精神分裂症的个人 (1998-2015).
- 将LSTM与指数级光滑,ARIMA,PSO,PSO-SVR和DNN模型进行比较.
主要成果:
- 在预测精神分裂症发病率方面,LSTM模型取得了最高的准确性.
- LSTM显示了最低的平均绝对百分比误差 (2.34),根平均平方误差 (157.42) 和平均平均误差 (154,831.70).
结论:
- LSTM模型是预测精神障碍发病率,特别是精神分裂症的可靠工具.
- 研究结果为政府管理人员和政策制定者提供了宝贵的见解,帮助他们制定临床策略,医疗保健教育和心理健康支持的财务规划.
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