Drug safety and automation intelligence
Keywords:
Drug, Adverse Events, Drug Safety, Artificial Intelligence.Abstract
In the area of digitalization, the documentation on patients of health systems (pharmacovigilance) is also being stored in electronic format. Because of this fact the volume of digital information generated in the hospitals is growing exponentially. Professionals often have to manage an excess of data and different kinds of information. The manner in which this sensitive information is presented to the doctors can help in the decision-making process and also alleviate the workload of several services within a hospital. All these facts make the creation of a robust system an important challenge for the Natural Language Processing, Text Mining & Artificial Intelligence research community. In this context the goal of this work is to obtain the Adverse Drug Reactions (ADRs) that are stated in the Electronic Health Records (EHRs) in a robust way. This need arises when experts have to prescribe a drug, since before that, they have to know if the patient has suffered from adverse reactions to substances or drugs. The final system should present the ADRs in the given EHR, showing the drug-disease pairs that triggered each ADR event. Today, the web has influenced people like never before. If a person wants to search an information, neither does he/she go to the library nor is he/she asks his/her friends and family, as there are many information sites on the web which provide a variety of information. The information is most of the times in either structured, semi structured or unstructured format. Efficient strategies for identification and extraction of information about adverse drug effects from free-text resources are needed to support pharmacovigilance research. Therefore, this work focuses on the adaptation of a machine learning-based relation extraction system for the identification and extraction of drug-related adverse effects from case reports. It relies on a ontology-driven methodology. Qualitative evaluation of the system show robust results.
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