使用自然语言处理和机器学习设计模型的协议,从电子健康记录中预测精神病的可能性
Icelini Stavers-Sosa1,2, David J Cronkite3, Lawrence D Gerstley4
1Department of Psychiatry, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA.
The Permanente journal
|September 2, 2024
概括
早期识别精神病谱系障碍 (PSD) 对于更好的结果至关重要. 这项研究使用电子健康记录和自然语言处理来预测年轻成年人的PSD风险,旨在缩短未经治疗的精神病的持续时间.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 及时诊断精神病谱系障碍 (PSD) 对有效治疗和改善患者结果至关重要.
- 迟到的早期精神病症状的识别往往导致长期未经治疗的精神病,对预后产生负面影响.
- 现有的诊断方法可能无法有效捕捉PSD的微妙早期指标.
研究的目的:
- 开发和验证一个预测模型,用于识别患有精神病谱系障碍高风险的年轻人 (15-29岁).
- 利用电子健康记录 (EHR) 数据和自然语言处理 (NLP) 进行早期PSD检测.
- 通过早期干预,减少未经治疗的精神病的持续时间.
主要方法:
- 追溯分析包括临床笔记和安全消息在内的电子健康记录数据,来自两个凯泽永久医院地区的15-29岁患者 (2017-2019).
- 开发使用三源NLP特征提取设计的预测模型,以在初级保健接触后12个月内识别新发病PSD的患者.
- 预测模型在每个区域内和组合样本上的验证.
主要成果:
- 该研究提出了一种新的方法,用于PSD风险预测,使用集成的EHR数据和NLP.
- 该模型旨在准确识别患有PSD风险较高的患者.
- 已经制定了验证策略,以评估模型的性能和通用性.
结论:
- 开发的模型有可能显著改善在年轻人群中早期发现精神病谱系障碍的潜力.
- 通过利用全面的EHR数据和先进的NLP技术,这种方法可以促进及时干预.
- 通过这种预测模型来减少未经治疗的精神病的持续时间,预计将导致长期患者的结果大大改善.
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