PDF(Pubmed)
Abstract：
OBJECTIVE: The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP).
METHODS: The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate.
RESULTS: The study\'s CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters.
CONCLUSIONS: The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.
METHODS: The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate.
RESULTS: The study\'s CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters.
CONCLUSIONS: The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.
摘要：
目的：本研究旨在提高头颈部癌症患者使用放射治疗的正常组织并发症概率（NTCP）的文献综述的效率和准确性。它采用荟萃分析（MA）和自然语言处理（NLP）。
方法：本研究由两部分组成。首先,它使用MA来评估口干症的NTCP模型，吞咽困难,放射治疗后的粘膜炎，使用Python3.10.5进行统计分析。第二,它将NLP与卷积神经网络（CNN）集成在一起，以优化文献检索，将3256篇文章减少到12篇。CNN设置包括50、50-200纪元范围的批量大小和0.001的学习率。
结果：该研究的CNN-NLP模型在使用Adamax优化200个时期后达到了0.94的显着准确性。MA显示早期效应口干症的AUC为0.67，晚期效应为0.74，表明中等到高的预测准确性，但在研究中具有高变异性。通过优化器和超参数，初始CNN精度从66.70%提高到94.87%。
结论：该研究成功合并了MA和NLP，确认特定模型-特征组合的高预测精度。它引入了基于时间的度量，每分钟字数（WPM），效率，并强调MA和NLP在临床研究中的实用性。
方法：本研究由两部分组成。首先,它使用MA来评估口干症的NTCP模型，吞咽困难,放射治疗后的粘膜炎，使用Python3.10.5进行统计分析。第二,它将NLP与卷积神经网络（CNN）集成在一起，以优化文献检索，将3256篇文章减少到12篇。CNN设置包括50、50-200纪元范围的批量大小和0.001的学习率。
结果：该研究的CNN-NLP模型在使用Adamax优化200个时期后达到了0.94的显着准确性。MA显示早期效应口干症的AUC为0.67，晚期效应为0.74，表明中等到高的预测准确性，但在研究中具有高变异性。通过优化器和超参数，初始CNN精度从66.70%提高到94.87%。
结论：该研究成功合并了MA和NLP，确认特定模型-特征组合的高预测精度。它引入了基于时间的度量，每分钟字数（WPM），效率，并强调MA和NLP在临床研究中的实用性。
