# Text Network Analysis to Develop a Search Strategy for a Systematic Review
## 4 Conclusions  
When establishing a literature search strategy for SRs, we were able to set research questions based on data through a TNA. The TNA allows us to correctly establish the PICO, visualize relationships between keywords, and effectively determine the priorities of research topics. Furthermore, by first establishing a systematic literature search strategy for a comprehensive literature review, we can enhance the overall reliability and validity of the SR. According to the centrality analysis results that quantified the importance of each keyword, it was discovered that various groups of diseases according to DC, BC, and CC are mixed, and it is known that the keywords are not independent but are connected. In this way, opportunities to discover new knowledge can be obtained through the TNA. However, there were several limitations to consider regarding the TNA as well. First, during the process of preprocessing keywords, researchers relied on their expertise and experience to refine the topics. Although this method based on expert knowledge was not a perfect method based on data, it was inevitable, as it was a researcher's unique field that computers could not yet judge. Second, the choice of which metric to focus on in centrality analysis to determine the importance of research topics--DC, BC, or CC--can vary. Generally, importance based on DC was chosen in healthcare [28; 29], but in individual academic fields, there may be cases where BC and CC are prioritized, leading to the limitation that the final judgment depends on the researcher's academic field. Nevertheless, this can be said to be diverse across academic fields. Based on the traditional search strategy for SRs, not using a TNA approach as we suggested, the included literature will be constructed with the researcher's discretion. For instance, when conducting a study on 'Health Impact Assessment of arsenic exposure', if the researcher selects 'arsenic' as the intervention and 'cancer' as the outcome, the selection of 'cancer' is entirely dependent on the researcher's own expertise. There are many other diseases besides cancer as health effects, but the remaining diseases cannot be ignored. Since there are so many 'diseases' as outcomes, problems arise as to which diseases to set first and how far to limit the scope of diseases. When many diseases are caused by a particular exposure, we need to know which diseases are of the highest interest and which diseases have been studied the most to date. This requires an objective and rational approach to disease terminology, which we propose. SR research questions are set through the TNA proposed in this study, it can be defined more clearly, and the reliability of the entire study can be improved. This not only increases the efficiency in the design and analysis stages of research but also clarifies the \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Degree Centrality (DC)** & \multicolumn{3}{c}{**Betweenness Centrality (BC)**} & \multicolumn{3}{c}{**Closeness Centrality (CC)**} \\ \hline
**1** & cancer & 7 & cardiovascular & 117 & congenital heart defects & 1 \\
**2** & kidney & 7 & toxicity & 113 & gestational exposure & 1 \\
**3** & cardiovascular & 6 & pregnancy & 75 & carcinogenesis & 0.5 \\
**4** & pregnancy & 6 & hypertension & 75 & gene-environment & 0.5 \\
**5** & toxicity & 5 & blood pressure & 64 & gene-metal interactions & 0.5 \\
**6** & bladder & 5 & bladder cancer & 53 & oncogenic & 0.5 \\
**7** & hypertension & 5 & birth weight & 36 & toxicants & 0.5 \\
**8** & birth weight & 4 & kidney & 33 & toxicogenomics & 0.5 \\
**9** & cytokine & 4 & lung cancer & 19 & basal cell carcinoma & 0.33 \\
**10** & preterm birth & 4 & maternal & 19 & diabetes mellitus & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of the centrality by DC, BC, and CC metrics and top 10 keywords. interpretation and significance of the results. In this study, we conducted the PICO setting through the TNA to establish the optimal literature search strategy based on data for SRs.