**2.2 Search criteria**


<sup>1</sup> Causality assessment of the ADRs is "method used for estimating the strength of relationship between drug exposure and occurrence of adverse reaction(s)" [5].

*Machine Learning Applications in Pharmacovigilance: Scoping Review DOI: http://dx.doi.org/10.5772/intechopen.107290*

Fields]) AND ("pharmacovigilance"[MeSH Terms] OR "pharmacovigilance"[All Fields]). While The keywords for the web of science are TOPIC: (machine learning) AND TOPIC: (pharmacovigilance). The number of hits in each database and the total number of hits obtained after applying the filters are shown in **Table 1**.

## **2.3 The articles selection**

All the articles found in the two bibliography databases were reviewed, the duplicate check was done, and 21 duplicates were detected and removed. After that, the remaining


#### **Table 1.**

*Shows the number of hits per database.*

**Figure 1.** *PRISMA diagram for the articles' selection process.*

hits were assessed. The inclusion criteria were peer-reviewed and relevant articles, the relevance means articles that clearly addressed the ML application in PV activities, while the exclusion criteria were articles that addressed PV alone, articles addressed the use of ML in drug-drug interaction (DDI) detection because DDI is not the focus of PV activities, and articles focused on considering more data sources rather than ML Applications.

The hits assessment process was done in three phases firstly assessment of the title, then an assessment of the information provided by the abstracts, eventually assessment of the full text. At each phase, articles were retained or excluded for analysis, based on the inclusion and exclusion criteria. PRISMA flow diagram was used to illustrate the selection process (**Figure 1**) [11].
