# The 2011-2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance
## 3 Pharmacokine Systems
### ADR Monitoring for Patient Safety  
Monitoring involves the continuous follow-up and safety management of a patient's condition, and can be said to be the ultimate purpose of pharmacovigilance. Table 4 lists the studies on monitoring [62; 63; 64; 65; 66; 67; 68; 69; 70; 71] in chronological order. We could investigate early studies conducted on a large-scale project basis [62; 66]. From 2009 to 2013, [PERSON] et al. [66] carried out the Monitoring Medicines (MM) Project that consisted of 11 consortium partners. Mobile  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Data** & **Objective** & **Methods** \\ \hline \multirow{4}{*}{[PERSON] et al. [52]} & \multirow{4}{*}{1. EHRs} & Develop molecular fingerprint-based models (MFBMs) to strengthen ADE signals generated from EHR data & 1. NLP 2. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [53]} & 1. EHRs 2. SRS 3. ADE databases 4. Bibliographic databases & Develop and evaluate methods in the EU-ADR project & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [54]} & 1. SRS 2. Bibliographic databases 3. ADE databases & Review similar reports in pharmacovigilance databases & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [55]} & \multirow{4}{*}{1. SRSs} & Present P method (PM) and evaluate its inter-rater reliability & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [56]} & \multirow{4}{*}{1. Bibliographic databases} & Evaluate how to improve detection of side effects in MeSH & 1. NLP 2. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [57]} & \multirow{4}{*}{1. Bibliographic databases} & Assess ADEs and their associated costs & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [58]} & 1. EHRs 2. SRSs & Evaluate the performance of Pharmacovigilance—Roussel Uclaf Causality Assessment Method (PV-RUCAM) & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [59]} & 1. SRSs 2. Social media & Assess the performance of statistical signal detection algorithms established in social media data & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [60]} & \multirow{4}{*}{1. Drug information databases 2. ADE databases} & Test the validity and reliability of the causality documentation (CausDoc) tool & 1. Statistical analysis \\ \hline \multirow{4}{*}{[PERSON] et al. [61]} & 1. EHRs 2. ADE databases & Development for pharmacovigilance to detect and evaluate ADE signals based on multicenter EHR & 1. Statistical analysis \\ \hline \hline \end{tabular} EHRs: electronic health records, ADE: adverse drug event, NLP: natural language processing, SRSs: spontaneous reporting systems, EU-ADR: Exploring and Understanding Adverse Drug Reactions, MeSH: Medical Subject Headings.  
\end{table}
Table 4: List of papers for assessing ADR signals and developing reference sets.  
applications have also been developed (e.g., MedWatcher) to improve the spontaneous reporting of patients and the management of reports with patient information [68].  
With the activation of social network systems (SNS), SNS data (e.g., Twitter and Facebook) have been analyzed as additional study data [69; 70]. Table 5 shows that recent studies use SRSs and social media data as the main analysis data sources and use EHR data or ADE databases as supplemental data [68; 69; 70; 71]. In addition to these recent trends, patient-generated data can be collected through health care services or wearable devices, which requires more diverse monitoring methods and protocols.