@Article{info:doi/10.2196/22860，作者=“陆兆华、王玉晓青、李新桐”，标题=“基于上下文检索、意见聚合和问答模型的COVID-19问题意见揭示:模型开发研究”，期刊=“J Med Internet Res”，年=“2021”，月=“3”，日=“19”，卷=“23”，数=“3”，页=“e22860”，关键词=“自然语言处理”;自动问答系统;语言总结;机器学习;生命和医学科学;COVID-19;公共卫生;背景:COVID-19对全球公共卫生构成挑战，因为它具有高度传染性，可能致命。已经出现了许多正在进行和最近发表的关于该疾病的研究。然而，关于COVID-19的研究在很大程度上仍在进行中，尚无定论。 Objective: A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. Methods: Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions. Results: We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19--related questions. Conclusions: Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19--related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years. ", issn="1438-8871", doi="10.2196/22860", url="//www.mybigtv.com/2021/3/e22860", url="https://doi.org/10.2196/22860", url="http://www.ncbi.nlm.nih.gov/pubmed/33739287" }