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使用混合RNN-LSTM模型预测巴基斯坦妇女的围产期抑郁症

Amna Zafar¹, Muhammad Wasim², Beenish Ayesha Akram³

¹Department of Computer Science, University of Engineering and Technology Lahore, Lahore, Pakistan.

PeerJ. Computer science

|March 10, 2025

概括

临产期抑郁症 (PND) 的早期预测对于母亲和婴儿的福祉至关重要. 一个新的深度学习模型在识别PND方面实现了95%的准确性,突出显示了巴基斯坦的高流行率.

科学领域:

医学研究医学研究
医疗保健中的人工智能
公共卫生公共卫生

背景情况:

围产期抑郁症 (PND) 影响母亲在怀孕期间和产后.
早期PND诊断具有挑战性,增加了孕产妇自杀和产后抑郁症的风险.
开发用于PND预测的AI模型的资源有限.

研究的目的:

开发和验证用于早期PND预测的新型深度学习框架.
为培训和评估PND检测模型策划一个独特的数据集.
为了解决 PND 数据集中的类失衡问题.

主要方法:

使用PHQ-9,EPDS和社会人口统计数据整理了PERI DEP数据集 (14,008条记录).
使用SMOTE和GAN过量采样来增强数据.
开发了一种混合重复神经网络 (RNN) 和长期短期记忆 (LSTM) 深度学习模型.

主要成果:

混合RNN-LSTM模型与SMOTE增强实现了95%的准确性和96%的F1得分.
在巴基斯坦的女性中发现了73.1%的PND患病率.
证明了该模型在预测PND方面的有效性.

结论:

开发的深度学习模型在预测PND方面表现出很高的有效性.
患病率很高,这凸显了巴基斯坦迫切需要PND预防和干预策略.
这项研究为全球解决PND提供了有价值的工具和见解.

关键词:

深度学习是一种深度学习.抑郁症抑郁症抑郁症心理健康心理健康周产期的周产期产后产后的产后在产前,产前生育.

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