用AI实时分析产后抑郁，准确率达90%
Detecting and explaining postpartum depression in real-time with generative artificial intelligence
- 融合NLP、机器学习与大模型，实现无创语音实时筛查
- 检测准确率90%，优于现有方法
- 解释性强，可向用户说明判断依据，适合临床使用
产后抑郁（PPD）是产妇分娩后严重影响身心健康的严重问题。快速识别PPD及其风险因素对及时干预至关重要。本文提出一种智能筛查系统，结合自然语言处理、机器学习与大语言模型（LLMs），实现低成本、实时、非侵入式的自由语音分析。该系统通过将LLMs与可解释的树基模型结合，利用特征重要性和自然语言生成，解决预测过程中的黑箱问题，使结果可解释。在多项评估指标上，系统检测准确率达90%，优于文献中已有方案。本研究有助于实现对产后抑郁及其风险因素的快速识别，为及时、精准的评估与干预提供支持。
原文摘要 · Abstract (English)
Among the many challenges mothers undergo after childbirth, postpartum depression (PPD) is a severe condition that significantly impacts their mental and physical well-being. Consequently, the rapid detection of ppd and their associated risk factors is critical for in-time assessment and intervention through specialized prevention procedures. Accordingly, this work addresses the need to help practitioners make decisions with the latest technological advancements to enable real-time screening and treatment recommendations. Mainly, our work contributes to an intelligent PPD screening system that combines Natural Language Processing, Machine Learning (ML), and Large Language Models (LLMs) towards an affordable, real-time, and non-invasive free speech analysis. Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMs with interpretable ml models (i.e., tree-based algorithms) using feature importance and natural language. The results obtained are 90 % on ppd detection for all evaluation metrics, outperforming the competing solutions in the literature. Ultimately, our solution contributes to the rapid detection of PPD and their associated risk factors, critical for in-time and proper assessment and intervention.
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