*3.3.2 Electronic health record mining*

*Discharge summaries mining:* The supervised machine learning technique was used to detect the ADRs from discharge notes in a tertiary hospital in Switzerland by using a hybrid method, ML, and rule-based. The manual annotation was used to create the training and testing datasets, while the supervised learning technique is used to classify the discharge notes as positive (had ADRs) or negative (had no ADRs), the automatic detection was efficient compared to the manual one and the accuracy was 0.90 [12]. Furthermore, ML algorithms were used to automate the detection of the relationship between the drug and the ADR from the discharge summaries [14].

<sup>2</sup> "A signal is defined by WHO as reported information on a possible causal relationship between an adverse event and a drug" [19].


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

