Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Sciences and Technology, Kaohsiung, Taiwan.
Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Cancer Control. 2024 Jan-Dec;31:10732748241286688. doi: 10.1177/10732748241286688.
This study explored the application of meta-analysis and convolutional neural network-natural language processing (CNN-NLP) technologies in classifying literature concerning radiotherapy for head and neck cancer. It aims to enhance both the efficiency and accuracy of literature reviews. By integrating statistical analysis with deep learning, this research successfully identified key studies related to the probability of normal tissue complications (NTCP) from a vast corpus of literature. This demonstrates the advantages of these technologies in recognizing professional terminology and extracting relevant information. The findings not only improve the quality of literature reviews but also offer new insights for future research on optimizing medical studies through AI technologies. Despite the challenges related to data quality and model generalization, this work provides clear directions for future research.
本研究探索了元分析和卷积神经网络-自然语言处理(CNN-NLP)技术在对头颈部癌症放射治疗相关文献分类中的应用,旨在提高文献综述的效率和准确性。通过将统计分析与深度学习相结合,本研究成功地从大量文献中识别出与正常组织并发症(NTCP)概率相关的关键研究。这证明了这些技术在识别专业术语和提取相关信息方面的优势。这些发现不仅提高了文献综述的质量,还为未来通过人工智能技术优化医学研究提供了新的见解。尽管存在数据质量和模型泛化相关的挑战,但这项工作为未来的研究提供了明确的方向。