# The 2011-2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance
## 4 Discussion  
Over the past 10 years, the numbers of pharmacovigilance studies have been increasing steadily. These studies have resulted in appropriate drug use regulations and guidance being issued. We found, through a literature review, that the main concerns in pharmacovigilance are the difficulty of standardizing data and terminology and establishing an \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Data** & **Objective** & **Methods** \\ \hline [PERSON] et al. [62] & 1. Drug information databases & \begin{tabular}{c} Provide a broad estimate of the need \\ for pharmacoepidemiologic resources \\ in the European Union (EU) \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [63] & 1. SRSs & \begin{tabular}{c} Systematic collection of more \\ thorough data on the safety of \\ atomoxetine and methylphenidate in \\ pediatric settings \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [64] & 1. Genetics and biochemical & \begin{tabular}{c} Compare the LIM diabetes \\ population with an external diabetes \\ reference population on \\ characteristics that may influence the \\ patient’s susceptibility for ADEs \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [65] & 1. EHRs & \begin{tabular}{c} Examine the use of retrospective \\ medication orders and inpatient \\ laboratory results documented in \\ the EHR \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [66] & 1. SRSs & \begin{tabular}{c} Support and strengthen consumer \\ reporting of ADEs and develop \\ methods to complement \\ spontaneous reporting \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] and [PERSON]. [67] & 1. EHRs & \begin{tabular}{c} Monitor, research, and add missing \\ information before drug marketing \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [68] & \begin{tabular}{c} 1. SRSs \\ 2. ADE databases \\ \end{tabular} & \begin{tabular}{c} Evaluate the quality of data collected \\ through a MedWatcher \\ \end{tabular} & 1. Statistical analysis \\ \hline [PERSON] et al. [69] & 1. Bibliographic databases & \begin{tabular}{c} Perform a methodical review to \\ characterize the different approaches \\ to ADEs detection/extraction from \\ social media \\ \end{tabular} & 1. Text mining \\ \hline [PERSON] et al. [70] & 1. SRSs & \
\begin{tabular}{c} Examine whether specific \\ product–adverse event pairs were \\ reported via social media before \\ being reported to FAERS \\ \end{tabular} & 1. Machine learning \\ \hline [PERSON] et al. [71] & 1. EHRs &
\begin{tabular}{c} Describe the impact of design choices \\ on the practical implementation of \\ pragmatic trials \\ \end{tabular} & 1. Statistical analysis \\ \hline \hline \end{tabular}  
ADE: adverse drug event, SRSs: spontaneous reporting systems, LIM: lareb intensive monitoring, EHRs: electronic health records, NLP: natural language processing, FAERS: The United States Food and Drug Administration Adverse Event Reporting System.  
\end{table} 
Table 5: List of papers for monitoring ADR and managing patient safety. ADR evaluation system. The databases and related websites provide mapping files or materials for terminology. However, even with expert guidance, pre-processing the data for practical analysis is time-intensive; many cases exist where a clinician or a pharmacist has to process the data manually. Therefore, reference standards could serve as ground truth for evaluating the ADR signals [53; 65; 70]. Many studies have developed new detection algorithms and detected drug safety indicators, but the results rarely lead to actual drug safety management. In addition, the studies using patient data are limited to single institution data, emphasizing the necessity of multicenter studies [72; 73; 74]. Thus, a platform that can comprehensively manage and systematically access all pharmacovigilance systems is required.