在不确定性下调查风险分析和影响自杀企图的因素的算法.
RuiHua Liang1, Ni Duan1, XueJing Liu1
1Department of Psychiatry, Qingdao Mental Health Center, Qingdao, 266000, Shandong, China.
Scientific reports
|May 7, 2025
概括
这项研究引入了一种新的风险评估模型,用于预测使用间隔值q-rung整形外科模糊 (ivq-ROF) 集和EDAS方法的自杀企图. 这种方法提高了心理健康干预和资源分配的准确性.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 模糊的逻辑 模糊的逻辑
背景情况:
- 自杀是一个重大的全球性挑战,需要改进风险评估工具.
- 目前的方法在风险因素评估中扎着不确定性.
研究的目的:
- 在风险不确定性条件下开发和验证针对自杀企图的新型风险评估模型.
- 为心理健康从业者和研究人员提高自杀风险预测的准确性.
主要方法:
- 使用间隔值的q-rung整形pair fuzzy (ivq-ROF) 集合信息,使用Sugeno-Weber聚合运算符和EDAS方法.
- 从平均值 (PDA) 和从平均值 (NDA) 应用正距离标准规范化和排名.
- 引入了用于模糊信息聚合的ivq-ROF Sugeno-Weber加权平均 (ivq-ROFSWWA) 和ivq-ROFS加权几何 (ivq-ROFSWG) 运算符.
主要成果:
- 拟议的模型通过结合PDA和NDA分数来准确识别关键风险因素.
- 与现有方法相比,该方法在预测自杀风险方面表现出更高的准确性.
- 该算法有效地处理复合数据接口,用于决策.
结论:
- 开发的分析框架为理解和减轻自杀风险提供了强有力的方法.
- 调查结果可以为心理健康政策,资源配置和干预策略提供信息.
- 该研究旨在通过增强分析能力,为减少未来的自杀行为做出贡献.
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