通过语言模型分析症状,优化疾病的分类
Esraa Hassan1, Tarek Abd El-Hafeez2,3, Mahmoud Y Shams4
1Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt. esraa.hassan@ai.Kfs.edu.eg.
Scientific reports
|January 17, 2024
概括
像MCN-BERT和BiLSTM这样的深度学习模型在从症状中预测疾病方面显示出高准确性. 这些先进的技术有望更早地检测疾病,改善远程诊断.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 从症状中自动预测疾病对于及时的医疗干预至关重要.
- 传统方法可能缺乏处理复杂症状数据的复杂性.
- 自然语言处理 (NLP) 和深度学习提供了新的方法.
研究的目的:
- 评估深度学习模型的有效性,特别是MCN-BERT和BiLSTM,用于自动化疾病预测.
- 通过不同的超参数调方法优化这些模型的性能进行比较.
- 评估模型预测疾病和识别药物不良反应 (ADR) 的能力.
主要方法:
- 使用了两个不同的数据集:数据集-1 (疾病症状组合) 和数据集-2 (用于ADR识别的Twitter数据).
- 采用了两个医疗概念规范化-双向编码器从变压器表示 (MCN-BERT) 模型和一个双向长短期记忆 (BiLSTM) 模型.
- 使用超参数调方法优化模型,包括AdamP,AdamW和Hyperopt.
主要成果:
- 使用AdamP的MCN-BERT模型获得了最高的准确性:数据集-1的99.58%和数据集-2.0的96.15%.
- 使用AdamW的MCN-BERT模型在数据集-1上达到98.33%的准确性,在数据集-2上达到95.15%的准确性.
- 使用Hyperopt的BiLSTM模型在数据集-1上显示了97.08%的准确性,在数据集-2上显示了94.15%的准确性.
结论:
- 深度学习模型,特别是MCN-BERT,显示出从症状描述中准确预测疾病的巨大潜力.
- 这些模型可以支持早期疾病检测,及时治疗,并增强远程诊断能力.
- 对NLP和医学应用的深度学习进行进一步的研究是有必要的.
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