Scholarlink

<p>This research work provides an innovative approach, called MediNet, for drug safety review classification that integrates the strengths of three word embedding approaches: FastText, ELMo, and GloVe, alongside an ensemble of EfficientNetB4 and MobileNet models. The unique blend of these word embeddings captures both context-independent and context-dependent representations, enabling the model to understand complex linguistic nuances within drug reviews. The ensemble architecture leverages EfficientNetB4’s scalability and MobileNet’s efficiency, making MediNet both powerful and resource-efficient. The proposed model MediNet is evaluated concerning performance on a comprehensive dataset of drug safety reviews, achieving remarkable results with a 95.69% accuracy, 96.46% precision, 98.30% recall, and 97.22% F1 score. The generalizability of MediNet is evaluated using the cross-validation technique, demonstrating the statistical significance of the results. Additionally, MediNet results are compared against six other well-known transfer learning models, where it consistently outperforms other models across all metrics. These results suggest that MediNet is a highly effective solution for classifying drug safety reviews, significantly improving accuracy and reliability compared to existing models. The proposed approach offers a promising direction for future research in natural language processing and its application to healthcare.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MediNet: ensemble transfer learning approach for classification of medical drugs-related text reviews using significant combined-embeddings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

查看全文
  • Tai-hoon Kim,
  • Asma Aldrees,
  • Dina Abdulaziz AlHammadi,
  • Muhammad Umer,
  • Taoufik Saidani,
  • Shtwai Alsubai,
  • Imran Ashraf

摘要

This research work provides an innovative approach, called MediNet, for drug safety review classification that integrates the strengths of three word embedding approaches: FastText, ELMo, and GloVe, alongside an ensemble of EfficientNetB4 and MobileNet models. The unique blend of these word embeddings captures both context-independent and context-dependent representations, enabling the model to understand complex linguistic nuances within drug reviews. The ensemble architecture leverages EfficientNetB4’s scalability and MobileNet’s efficiency, making MediNet both powerful and resource-efficient. The proposed model MediNet is evaluated concerning performance on a comprehensive dataset of drug safety reviews, achieving remarkable results with a 95.69% accuracy, 96.46% precision, 98.30% recall, and 97.22% F1 score. The generalizability of MediNet is evaluated using the cross-validation technique, demonstrating the statistical significance of the results. Additionally, MediNet results are compared against six other well-known transfer learning models, where it consistently outperforms other models across all metrics. These results suggest that MediNet is a highly effective solution for classifying drug safety reviews, significantly improving accuracy and reliability compared to existing models. The proposed approach offers a promising direction for future research in natural language processing and its application to healthcare.