人工智能在预防和早期发现产后抑郁症方面:系统性审查和元分析
Azahara Ruger-Navarrete1, María Gómez-Ferrera2, Beatriz Mérida-Yáñez1
1Department of Nursing, Faculty of Health Sciences of Ceuta, University of Granada, Ceuta, Spain.
Frontiers in psychiatry
|February 5, 2026
概括
人工智能 (AI) 在早期产后抑郁症检测方面表现有前途. 与健康数据集成的机器学习模型提供了更高的准确性,但在广泛的临床应用中必须解决道德和技术方面的挑战.
科学领域:
- 生殖健康 生殖健康
- 医疗信息学 医疗信息学
- 计算医学是一种计算医学.
背景情况:
- 产后抑郁症 (PPD) 显著影响母亲的健康,婴儿的发育和家庭的福祉.
- 早期发现和预防PPD对于减轻不良后果至关重要.
研究的目的:
- 评估人工智能 (AI) 在产后抑郁症的早期发现和预防中的作用.
- 系统地审查和对应用人工智能用于PPD识别的研究进行元分析.
主要方法:
- 在Scopus,PubMed,科学网络和CINAHL (2020-2025) 进行系统的文献搜索.
- 根据PRISMA指南进行研究选择和评估.
- 两个随机效应的元分析,以估计人工智能模型用于PPD检测的聚合灵敏度和准确性.
主要成果:
- 16项研究符合纳入标准,分析了1857个记录.
- 与传统方法相比,机器学习模型 (随机森林,XGBoost,神经网络) 显示出更高的准确性.
- 人工智能与医疗记录和社交媒体数据的整合促进了早期,个性化的PPD检测. 聚合灵敏度: 69%,准确度: 79%. 总体灵敏度: 69%,准确度: 79%. 总体灵敏度: 69%,准确度: 79%.
结论:
- 人工智能对改善产后抑郁症检测和预防策略具有重大潜力.
- 解决算法偏见,数据隐私问题和实施障碍对于人工智能的公平临床整合至关重要.
- 需要进一步的研究和开发来克服技术和教育方面的挑战,以便在PPD护理中有效地部署AI.
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