# The 2011-2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance
## 3 Pharmacokine Systems
### Data Collection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Data** & **Objective** & **Method(s)** \\ \hline \multirow{4}{*}{[PERSON] et al. [19]} & \multirow{4}{*}{1. SRSs} & Legislation and regulatory framework, as well as financial support to build pharmacovigilance systems are needed & - \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & 1. SRSs & & 1. Text mining \\  & 2. Drug information databases & Presenting the current status of the & 2. NLP \\  & 3. ADE databases & clinical decision support system & 3. Machine learning \\  & 4. Genetics and biochemical & (CDSS) & 4. Deep learning \\  & 5. Bibliographic databases & & 5. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & 1. SRSs & & \\  & 2. Drug information databases & Discuss the contribution of & \\  & 3. Genetics and biochemical & pharmacovigilance & 1. Statistical analysis \\  & 4. databases & & \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & 1. Drug information databases & Build a semantic resource based on & \\  & 1. Drug information databases & formal description logic to aid the & \\  & 1. Drug information databases & generation of on-demand custom & 1. Statistical analysis \\  & & groupings by appropriately selecting & \\  & & terms: OntoADR & \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & 1. EHRs & Presenting methods to discover & 1. NLP \\  & 2. Drug information databases & confounding variables based on & 2. Statistical analysis \\  & 3. Bibliographic databases & scalable literature & \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & 1. SRSs & & 1. NLP \\  & 2. Drug information databases & Drug safety evaluation & 2. Machine learning \\  & 3. ADE databases & & \\ \hline \multirow{4}{*}{The Knowledge Base \begin{tabular}{c} workgroup of the \\ Observational Health Data \\ Sciences and Informatics \\ (OHDSI) collaborative. \\ [25] \\ \end{tabular} } & 1. EHRs & & \\  & 2. SRSs & Introduce the structure and & 1. NLP \\  & 3. SPLs & functionality of the Largescale & 2. Machine learning \\  & 4. Genetics and biochemical & Adverse Effects Related to Treatment & 3. Statistical analysis \\  & 4. databases & Evidence Standardization (LAERTES) & \\  & 5. Bibliographic databases & & \\ \hline \multirow{4}{*}{[PERSON] et al. [26]} & 1. SRSs & \
\begin{tabular}{c} Understand the important role of \\ pharmacovigilance as a \\ historical phase \\ \end{tabular} & - \\  & 2. ADE databases & & \\ \hline \multirow{4}{*}{[PERSON] et al. [27]} & 1. EHRs & & \\  & 2. Genetics and biochemical & & \\  & databases & & \\ \hline \multirow{4}{*}{[PERSON] et al. [28]} & 1. SRSs &
\begin{tabular}{c} The importance of policy framework \\ in relation to pharmacovigilance is \\ discussed in detail \\ \end{tabular} & 1. Machine learning \\  & 2. Statistical analysis & \\ \hline \hline \end{tabular}  
* SRSs: spontaneous reporting systems, NLP: natural language processing, ADE: adverse drug event, EHRs: electronic health records, SPLs: structured product labels.  
\end{table} 
Table 2: Selected papers and their sources of data for determining drug safety signals. [PERSON] et al. [29] published the most recent review article on different pharmacovigilance databases, including VigiBase of the WHO [30], EudraVigilance of the European Medicines Agency (EMA) [31], the FDA Adverse Event Reporting System (FAERS) of the United States Food and Drug Administration [32], and the French pharmacovigilance database (FPDB) [33]. Additionally, databases are available for nationally managed systems, clinical institution data, drug information, and from the literature; for example, Sentinel [34], Exploring and Understanding Adverse Drug Reactions (EU-ADR) [35], EHR [36], DrugBank [37], and MEDLINE [38].