使用自然语言处理预测美国退伍军人中自杀的情况,丰富了健康的社会和行为决定因素
Avijit Mitra1, Kun Chen2, Weisong Liu3
1University of Massachusetts Amherst.
Research square
|May 15, 2024
概括
电子健康记录中的健康的社会和行为决定因素 (SBDH) 显著改善了自杀风险预测模型. 自然语言处理 (NLP) 增强了SBDH的识别,提高了退伍军人的模型性能.
科学领域:
- 医疗信息学 医疗信息学
- 临床信息学 临床信息学
- 计算医学是一种计算医学.
背景情况:
- 健康的社会和行为决定因素 (SBDH) 对于预测自杀风险至关重要,但在预测模型中未得到充分利用.
- 富含SBDH信息的非结构化电子健康记录 (EHR) 数据在自杀预测方面仍然基本未被开发.
结论:
- 通过NLP从EHR中提取的SBDH在各种时间范围内大大提高了自杀预测的准确性.
- 通过NLP利用非结构化EHR数据为主动自杀风险评估提供了一个有希望的途径.
- 这些发现为医疗保健提供者和政策制定者提供了宝贵的见解,以加强自杀预防策略.
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