# Text Network Analysis to Develop a Search Strategy for a Systematic Review
## 2 Materials and Methods
### Data Collection
#### 2.1.3 Keywords Extraction  
We extracted keywords from the selected papers and entered them row-by-row for each article in Excel. After improving the accuracy of the TNA, the following procedure was used for keywords. First, we removed 'arsenic' keywords, as it may be duplicated in all studies since the study aims to investigate the health effects of arsenic exposure. Second, other heavy metals (cadmium, lead, and mercury) were also removed when they were set as duplicates for heavy metal exposure since they are not relevant. Third, research methodologies (clinical trial, systematic review, and meta-analysis) that were not directly related to health effects were removed. Lastly, words used with the same meaning, such as 'cancer risk' and 'cancer,' were integrated into one word like 'cancer'. As a result of this process, the number of keywords was reduced to 133 (without duplicates) (Table 2).  
Additionally, there is a need to further structure keywords into disease-oriented groups. We removed minor keywords with relatively low connectivity and not belonging to the disease group category (e.g., country names, rural health, and community). As  
\begin{table}
\begin{tabular}{l l l l} \hline \hline Database & \
\begin{tabular}{l} PubMed \\ [[https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)]([https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)) (accessed on 25 September 2024) \\ \end{tabular} \\ \hline Search Date & 30 April 2024 \\ \hline Search Query &
\begin{tabular}{l} (’Arsenic’ [Mesh] OR 'Arsenic Trioxide’ [Mesh] OR 'Arsenic Poisoning’ [Mesh] OR 'Arsenic’ [tiab] OR 'Arsenic Trioxide’ [tiab] OR 'Arsenic Poisoning’ [tiab] OR 'Arsenicals’ [tiab]) AND \\ (systematicreview [Filter]) \\ \end{tabular} \\ \hline Results & 212 papers \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant information for collecting papers and number of papers searched.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Keyword 1** & **Keyword 2** & **Keyword 3** & **Keyword 4** \\ \hline
**1** & blood pressure & hypertension & \\
**2** & diabetes mellitus & urinary biomarkers & microglobulin & albumin \\
**3** & stroke & & \\
**4** & Argentina & cancer & health & \\
**5** & bladder cancer & lung cancer & \\  & &... &... &... \\
**76** & C-reactive protein (CRP) & cytokine & immunotoxicity & inflammation \\
**77** & blood pressure & hypertension & \\
**78** & cardiovascular diseases & lipid metabolism & \\
**79** & kidney & kidney diseases & proteinuria & \\
**80** & birth weight & maternal & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of data consisting of extracted keywords for each paper.  
a result, the total number was reduced to 59 (Table 3). We created a new dataframe with a 'Keywords' and a 'Group' column to group the disease for each keyword into seven categories: childbirth, pregnancy, heredity, cancer, heart and blood, toxicity, and metabolism. Childbirth-related diseases were defined as those that can occur in the fetus, while Pregnancy-related diseases were defined as those that can occur in the mother. The distribution of frequencies by each disease group is shown in a pie chart (Figure 3). Metabolic diseases account for the largest proportion, approximately 31%, followed by cancer diseases (20%) and heart and blood diseases (15%).