注意启用集体深度学习模型及其对抑郁症检测的验证:一个域采用范式
Jaskaran Singh1, Narpinder Singh2, Mostafa M Fouda3
1Department of Computer Science, Graphic Era, Deemed to be University, Dehradun 248002, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
注意启用集体深度学习 (aeEDL) 模型显著提高了不同领域的抑郁症检测准确性. 这些先进的模型优于传统方法,为识别抑郁症症状提供了更广泛,更有效的方法.
科学领域:
- 计算语言学和自然语言处理.
- 人工智能和机器学习在医疗保健中的应用.
- 精神病学信息学和计算精神病学.
背景情况:
- 抑郁症是全球日益严重的健康问题,自杀风险增加.
- 在跨域设置中使用文本分析准确检测抑郁症仍然是一个重大挑战.
- 现有的单独深度学习 (SDL) 和集体深度学习 (EDL) 模型缺乏足够的稳定性.
研究的目的:
- 调查注意力启用集体深度学习 (aeEDL) 架构在抑郁症检测中的有效性.
- 为了比较aeEDL与注意力不启用SDL (aneSDL),注意力启用SDL (aeSDL) 和注意力不启用EDL (aneEDL) 模型的性能.
- 验证aeEDL在用于抑郁症检测的跨领域情绪分析中的可通用性和有效性.
主要方法:
- 开发基于EDL的架构,包括SDL和EDL模型的注意力块.
- 在四个特定领域的数据集上培训和评估11个SDL和5个EDL模型.
- 使用"已见"和"未见"范式 (SUP) 的科学验证以及与SemEval (2016) 数据集的基准测试.
主要成果:
- 与相应的SDL组件相比,EDL模型的平均精度增加了4.49%.
- 注意力机制提高了 aeSDL 与 aneSDL 的平均精度 (AUC) 2.58% (1.73%) 和 aeEDL 与 aneEDL 的 2.76% (2.80%).
- 在SemEval数据集中,aeEDL模型的表现始终优于SDL模型,最佳aeEDL (ALBERT+BERT-BiLSTM) 在SemEval数据集中超过最佳aeSDL (BERT-BiLSTM) 3.86%.
结论:
- 注意启用集体深度学习 (aeEDL) 架构在跨域设置中的抑郁症检测方面优越.
- 拟议的aeEDL方法显示出高效率和通用性,符合严格的验证标准.
- 这项研究验证了将注意力机制纳入EDL模型的假设,该假设显著提高了抑郁症症状检测.
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