摘要：Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications.
