青少年心理健康的机器学习见解:功能性沟通,治疗和教育的数据驱动方法
Dafni Patsiala1, Anastasia Mantesi1, Magdalini Agrafioti1
1Ocelot Special Psychosocial Intervention Unit-f.C.T.E Society, Athens, Greece.
Studies in health technology and informatics
|July 1, 2025
概括
这项研究确定了青少年因犯罪而被转诊的常见诊断,发现高动力和家庭相关问题很普遍. 数据分析揭示了不同的子组,以指导有针对性的心理健康干预.
科学领域:
- 青少年心理健康 青少年心理健康
- 临床信息学 临床信息学
- 在医疗保健中的数据科学.
背景情况:
- 被指控犯罪的青少年往往存在复杂的心理健康需求.
- 在这一群体中识别高风险人群是有效干预的关键.
- 常规收集的临床数据有潜力识别这样的个人资料.
研究的目的:
- 分析犯罪指导青少年的诊断模式.
- 用数据驱动方法确定临床连贯的子组.
- 从电子健康记录中显示出高风险个人资料的工作流程.
主要方法:
- 对152名犯罪指导青少年 (11-21岁) 的队列分析.
- 排名和绘制20个最常见的国际疾病分类,第10版 (ICD-10) 代码.
- 应用K-Means集群模型,可视化与t分布式静态邻居嵌入 (t-SNE),使用年龄和ICD-10标志.
主要成果:
- 超运动 (F90.x) 和与家庭有关的心理社会 (Z63.5) 诊断是最常见的.
- 该K-Means模型划分了四个临床上一致的子组.
- 工作流程有效地识别了高风险的个人资料.
结论:
- 常规收集的数据可以快速分析,以确定青少年的心理健康需求.
- 数据驱动的分组有助于理解犯罪指导的年轻人的不同临床表现.
- 这种方法可以为有针对性的青少年心理健康干预提供信息.
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