## 3 Materials and Methods  
### Data Collection and Preprocessing  
#### 3.1.1 Data Collection  
Data for bibliometric analysis concerning the NLP-enhanced clinical trial research were drawn from the WoS, PubMed, and Scopus databases. WoS is a quality-assured database for evaluating research frontiers of particular research fields [67, 68]. We also included relevant papers from PubMed as it is the most comprehensive medical literature database worldwide. The PubMed database is provided by the National Library of Medicine, covering the most significant number of publications on life sciences and biomedical research. In addition, we also included Scopus, which is the most reliable [69] and comprehensive [70] academic database of peer-reviewed literature, including scientific journals, books, and conference proceedings. In the WoS, we adopted \"TS\" as a search field, as suggested by [PERSON] et al. [71]. Papers indexed in Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities Citation Index, Conference Proceedings Citation Index- Science, and Conference Proceedings Citation Index - Social Science & Humanities were considered. In PubMed, [Title/Abstract] was used as a search field [72]. Literature type included article and proceedings papers written in English. Usually, research articles contain more original findings. In addition, a great deal of NLP research papers is covered by conferences. One critical procedure in data retrieval was to identify a set of keywords related to NLP and clinical trials. The most challenging task was to maximize the identification of relevant studies. For example, publications specific to NLP research may not mention terms like \"natural language processing;\" thus, a query for retrieving NLP research literature should include field-specific terms like \"semantic analysis,\" which was relevant to NLP. We followed steps proposed by [PERSON] et al. [73] to attain the keywords list for NLP. Step One: A seed list (e.g., \"natural language processing,\" \"semantic analysis,\" \"bag of words,\" \"word sense disambiguation\") was provided by NLP domain experts based on their expertise and understanding of NLP. Step Two: The seed keywords were adopted to retrieve papers in the WoS. Step Three: We identified the author-defined keywords contained in those indexed as highly cited papers among the retrieved papers, and then presented them to NLP domain experts to exclude irrelevant ones. The relevant ones were added to the seed list. Similarly, we used the same steps to attain a keywords list for clinical trials with seed keywords, including \"clinical trial\",\" \"randomized controlled trial\",\" and \"clinical research trial\".\" The retrieval was conducted on April 10, 2019. We chose 1999-2018 as the study period. The final keyword queries for data retrieval are shown in Tables 12 and 13 in the Appendix A. Using the queries, 1595, 512, and 1580 papers were retrieved. The total retrieved 3687 papers were further filtered to guarantee a close relation to the research target, which was conducted by two domain experts (A and B) with comprehensive knowledge and a deep understanding of the interdisciplinary area. Expert A reviewed all the articles based on the criteria listed in Table 2 and finalized 451 papers for further data analysis. Expert B conducted the same filtering process, but for only 100 randomly selected papers. The consistency rate in the decisions made by the two experts was above 95%. Thus, the 451 articles were used as the final dataset, including 194 from the WoS, 25 from PubMed, and 232 from Scopus. As the WoS has provided the research domain of each paper, we analyzed research domains of these papers as inspired by [PERSON] et al. [74], the result of which is shown in Table 3. It should be noted that as one paper might originate with more than one domain, we included all of them in our analysis.