用不完整的采访数据预测学校暴力风险:一种自动化评估方法
Lara J Kanbar1, Alexander Osborn2, Andrew Cifuentes2
1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States.
JAMIA open
|August 1, 2025
概括
一个新的机器学习算法,自动化RIsk评估 (ARIA),使用NLP预测学校的侵略风险从面试. 即使在不完整的评估中,ARIA也表现出强的表现,有助于学校安全工作.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 犯罪学 犯罪学
背景情况:
- 学校暴力风险评估传统上依赖于手动,耗时和主观的方法.
- 开发客观和有效的工具对于有效的预防策略至关重要.
研究的目的:
- 开发和评估一种机器学习算法,即自动化RIsk评估 (ARIA),用于使用NLP预测侵略风险.
- 用不完整的采访数据来评估算法的性能.
主要方法:
- 在标准化面试问题上,ARIA使用自然语言处理 (NLP) 来识别具有攻击性预测力的语言模式.
- 功能集被逐渐添加,模拟部分采访,并使用L2-规范化后勤回归和L2-SVM分类器进行评估.
- 数据包括来自儿童和青少年侵略性简要评分 (BRACHA) 和学校安全量表 (SSS) 仪器的412次采访.
主要成果:
- 在仅仅10个BRACHA问题之后,ARIA实现了0.9的ROC曲线下的区域,即使在截断的采访中也显示出高的预测能力.
- 当使用不完整的数据时,算法的性能仍然很强大,与完整的评估相提并论.
- 完整的BRACHA和BRACHA + SSS评估显示了类似的绩效水平.
结论:
- 当面试无法完全完成时,ARIA为风险评估提供了一个可行的解决方案.
- 该算法可以通过从部分数据提供可靠的风险预测来减少学校人员的负担.
- 这项技术有可能提高学校的安全性和预防暴力的努力.
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