人工智能和机器学习技术用于自杀预测:整合饮食模式和环境污染物
Mayyas Al-Remawi1, Ahmed S A Ali Agha2,1, Faisal Al-Akayleh1
1Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy and Medical Sciences, University of Petra, Amman, Jordan.
Heliyon
|December 25, 2024
概括
人工智能 (AI) 和机器学习 (ML) 正在彻底改变预防自杀的方法. 这些技术通过整合各种数据来实现高预测准确性,为干预策略提供了新的希望.
科学领域:
- 数字健康数字健康
- 计算精神病学是一种计算精神病学.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 自杀是一个关键的全球健康问题,每年导致近80万人死亡,特别影响年轻人.
- 迫切需要创新的自杀预防策略,超越传统方法.
研究的目的:
- 探索人工智能 (AI) 和机器学习 (ML) 在自杀风险预测和预防中的作用.
- 检查AI/ML如何整合复杂的风险因素 (精神病,社会经济,饮食,环境) 以加强干预.
主要方法:
- 使用PubMed和Web of Science对AI/ML研究预防自杀的综合文献综述.
- 评估AI/ML模型 (例如,随机森林,神经网络),分析电子健康记录,社交媒体和数字行为.
- 对饮食因素和环境污染物 (,,等) 的评估. 与自杀行为有关的.
主要成果:
- 通过整合多种数据源,AI/ML模型显示出高预测准确度 (近90%).
- 人工智能/ML方法为地理和人口统计量身定制的公共卫生干预提供了潜力.
- 虚拟治疗师和聊天机器人等人工智能驱动的工具可以提供立即支持,特别是在服务不足的地区.
结论:
- 人工智能和机器学习正在改变自杀预防,需要全面的模型来解决多因素风险.
- 对于在心理健康领域负责任地部署人工智能,必须考虑道德问题和隐私问题.
- 基于人工智能的工具对于立即提供心理健康支持和加强全球自杀预防策略至关重要.
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