[论文解读] Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

Yujia Bao, Zhengyi Deng+10 more|arXiv (Cornell University)|Apr 24, 2019

Biomedical Text Mining and Ontologies参考文献 24被引用 16

一句话总结

本研究开发并评估了两种机器学习模型——支持向量机（SVM）和卷积神经网络（CNN）——以自动分类生物医学文献摘要，判断其是否与癌症易感基因的外显率或患病率相关。SVM在外显率分类中达到89.53%的准确率，在患病率分类中达到89.14%，表明其在临床遗传学中大规模文献综述方面具有高性能表现。

ABSTRACT

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date.

研究动机与目标

• 通过自动化相关摘要的分类，应对生殖系遗传学文献快速增长的挑战。
• 开发机器学习工具，协助临床医生识别关于癌症易感基因外显率与患病率的关键研究。
• 构建一个包含3,740个（外显率）和3,753个（患病率）摘要的标注数据集，用于训练和评估分类模型。
• 比较传统SVM与深度学习CNN模型在相同生殖系变异相关性分类任务中的性能表现。
• 实现可扩展、持续更新的知识库维护，用于遗传性癌症风险评估的临床决策支持系统。

提出的方法

• 通过PubMed文献检索，收集与生殖系癌症易感基因相关的标题与摘要。
• 由专家评审对3,740篇摘要进行外显率相关性标注，对3,753篇摘要进行患病率相关性标注。
• 使用文本特征的n-gram袋模型训练支持向量机（SVM）模型。
• 使用原始文本输入训练卷积神经网络（CNN）模型，以学习非线性决策边界。
• 将数据划分为60%用于训练，20%用于超参数调优，20%用于评估，适用于两种模型。
• 使用准确率作为主要指标，在保留的测试集上评估模型性能。

实验结果

研究问题

• RQ1 机器学习模型能否准确分类生物医学摘要，判断其是否与癌症易感基因外显率相关？
• RQ2 机器学习模型能否准确分类摘要，判断其是否与癌症易感基因患病率相关？
• RQ3 传统SVM与深度学习CNN模型在分类生殖系变异文献方面的性能表现如何比较？
• RQ4 自动化分类在多大程度上可减轻临床遗传学中手动文献综述的工作负担？
• RQ5 这些模型能否支持临床决策支持工具所用知识库的持续更新与维护？

主要发现

• SVM模型在外显率相关摘要分类中达到89.53%的准确率。
• CNN模型在外显率分类中达到88.95%的准确率，尽管模型结构更复杂，仍表现出强劲性能。
• 在患病率分类中，SVM模型达到89.14%的准确率，CNN模型达到89.13%。
• 两种模型均表现出高且相近的准确率，表明对于此类任务，SVM等简化模型同样有效。
• 结果表明，自动化分类工具可显著减少监测和更新生殖系癌症风险知识所需的手动工作量。
• 本研究证明了利用自然语言处理与机器学习技术，实现临床遗传学应用中文献筛选的可扩展化是可行的。