[基于社区的精神分裂症谱系障碍患者暴力复发的动态预测:一个共同的模型]
Xiangrui Wu1, Xianmei Yang1, Ruoxin Fan1
1/ （ 610041） Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
概括
这项研究开发了一种联合建模方法,用于预测精神分裂症谱系障碍的社区患者的暴力复发. 与传统的静态模型相比,新方法显示出更高的预测准确性,为干预系统提供了洞察力.
科学领域:
- 精神病学是一个精神病学.
- 临床心理学 临床心理学
- 生物统计学 生物统计学
背景情况:
- 精神分裂症谱系障碍患者 (SSDP) 在社区中可能表现出暴力行为.
- 预测暴力再犯对于有效的干预策略至关重要.
- 传统的模型可能无法完全捕捉影响暴力事件复发的因素的动态性质.
研究的目的:
- 使用联合建模方法,在社区SSDP中构建暴力复发的预测模型.
- 将联合模型的性能与传统静态模型进行比较.
- 确定与该人口中暴力复发相关的关键因素.
主要方法:
- 分析了一组4565名社区SSDP的基线暴力行为.
- 增长混合模型 (GMM) 确定了药物坚持和社会功能模式.
- 一个结合模型,包括一个零膨胀负二项式回归,被装配并使用10倍交叉验证进行验证.
主要成果:
- 与静态模型相比,联合模型表现出较低的Akaike信息标准 (AIC),根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 暴力复发的预测因素包括年龄,婚姻状况,教育,经济状况,历史暴力,药物坚持模式和社会功能模式.
- 大约3.44%的患者在暴力中经历了重复犯罪.
结论:
- 联合建模是通过计算动态变量来预测暴力复发的有效策略.
- 开发的联合模型提供了比传统方法更好的预测准确度.
- 调查结果可以为SSDP的综合干预系统的开发提供信息.
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