## 3 Materials and Methods
### Data Collection and Preprocessing
#### 3.1.2 Data Pre-Processing  
Full bibliographic information of the 451 articles was downloaded in comma separated values (CSV) or plain text file (TXT) files. The CSV or TXT files contained bibliographic details such as authors' names, affiliation, publication year, publication sources, title, abstract, and keywords information. We performed feature extraction and pre-processing. Key features such as title, abstract, keywords, publication source, publication year, and author address of each paper were extracted using a Python program and stored in Microsoft Excel for further pre-processing, as described in the following. First, check all the papers to make sure all features were included in the Excel file. In particular, use Python to check the integrity of the authors' address information automatically. For those without complete information, manual supplements of information were conducted based on the original text of the paper. Second, use Python to extract the information of authors, affiliations, and countries/regions from the authors' addresses and to further transform into analyzable forms. An example of the authors' addresses transformation is shown as Table 4. Third, carry out normalization. For example, for the analysis of prolific countries/regions, articles originating from England, Scotland, Wales, and Northern Ireland were combined as the UK, while articles originating from Hong Kong, Macau, and Taiwan were individually calculated. For the analysis of prolific affiliations, expressions of the same affiliation were unified. For example, Educ Univ Hong Kong, Hong Kong Inst Educ, Educ Univ Hong Kong, The Education University of Hong Kong, Hong Kong Institute of Education, and The Hong Kong Institute of Education were unified as the Education University of Hong Kong. For science mapping analysis, author-defined keywords, and the KeyWords Plus, PubMed MeSH, or Scopus Index keywords, were adopted. Keywords pre-processing was implemented to enhance analysis efficiency. Firstly, keywords were manually assigned to papers without keyword information, according to paper contents, especially titles. For instance, keywords \"Ontology,\" \"data management system,\" \"post-genomic clinical trial,\" \"Grid infrastructure,\" and \"cancer research\" were extracted from the title \"Ontology based data management systems for post-genomic clinical trials within a European Grid infrastructure for cancer research.\" Then, all the keywords were expressed in lower case and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Type** & **ID** & **Criteria** \\ \hline \multirow{4}{*}{Clinical trials research} & Inclusion criteria & I1 & Randomized controlled trials \\  & & I2 & Clinical research studies \\  & & E2 & Not focused on clinical trial \\  & Exclusion criteria & E3 & Not a scientific research \\  & & E4 & Without abstract \\  & & I1 & Use of NLP algorithms/approaches/technologies \\  & Inclusion criteria & I2 & Improvement of NLP technology/algorithm \\ NLP research & & E1 & Use of pure mathematical or statistical algorithms \\  & Exclusion criteria & E4 & Without abstract \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of inclusion and exclusion criteria for the manual verification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Domain** & **Paper Count** & **Proportion** \\ \hline Computer Science & 93 & 23.66\% \\ Medical Informatics & 79 & 20.10\% \\ Health Care Sciences \& Services & 50 & 12.72\% \\ Mathematical \& Computational Biology & 18 & 4.58\% \\ Engineering & 16 & 4.07\% \\ Information Science \& Library Science & 16 & 4.07\% \\ Psychology & 13 & 3.31\% \\ Public, Environmental \& Occupational Health & 12 & 3.05\% \\ Research \& Experimental Medicine & 7 & 1.78\% \\ Science \& Technology - Other Topics & 7 & 1.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top research domains for the 194 papers from the Web of Science (WoS). singular form. Keywords with similar concept meanings were grouped.