# Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics
## 1 Introduction  
Drug product labels are regulatory documents required as part of the marketing authorization of each medicine. They provide up-to-date and comprehensive information about the risks, benefits, and pharmacological properties of marketed medicines. As such, extracting the clinical knowledge stored in product labels and making it available in the form of computationally accessible knowledge bases would benefit several applications in the area of drug safety surveillance and assessment. For example, during post-marketing safety assessments, it is crucial to determine whether an investigated adverse drug reaction (ADR) is already labeled [1, 2].  
There are two main versions of drug product labels around the world: Structured Product Labels (SPL) introduced by the Food and Drug Administration (FDA) in the US, and the Summary of Product Characteristics (SmPC) supervised by the European Medicines Agency (EMA) in the EU [3]. Both product labels provide information in sections, and the content of each section is in free narrative texts. This study focuses on the undesirable side effect section of product labels which describes adverse drug reactions (ADRs).  
To date, there are no structured machine-readable ADRs. Therefore, extracting ADRs from unstructured product labels becomes an interesting research topic. There are many studies focusing on transforming unstructured ADRs data into structured machine-readable data [4, 5, 6, 7, 8]. However, the majority of current studies focus on the US version of product labels. For example, systems such as SPLICER and SPL-X were developed to extract ADR terms from particular sections using various natural language processing(NLP) techniques, including named entity recognition, rule-based parsing, and NegEx [4]. [PERSON] et al. proposed to use open-source NLP tools to extract drug indication information from SPL [5]. [PERSON] et al. managed to fetch ADR terms using Oracle Text search from 1164 single-ingredient medicines [6]. A variety of NLP techniques were designed to extract ADRs from SPL in the 2017 Text Analysis Conference track [2].  
However, the FDA SPL is the main data source used in such studies. Until now, the SmPC, as the European equivalent to SPL, has barely been investigated and no method has been developed solely for extracting ADRs from the SmPC. This study contributes to the field as a starting point.  
In this study, the main research question we are addressing is how to utilize NLP techniques to automatically extract ADRs from standardized European product labels, namely SmPC. To answer the question, we first develop an NLP pipeline to extract adverse drug reactions from SmPC. A knowledge base is created from the extracted terms. The main characteristics of the NLP pipeline and the knowledge base are summarized as follows: (a) open-source and reproducible; (b) customizable for related ad hoc tasks; (c) knowledge base applicable for clinical studies.  
The paper is structured as follows. Section 2 illustrates a number of methods and materials used in our study. Then the results are presented in Section 3. Discussions in Section 4 summarize the main findings of this research and some limitations. Section 5 concludes the study.