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\begin{document}

\title{Drug-Drug Interaction Extraction-Based System: an NLP Approach}



\author[1]{Regina Sousa}%
\author[1]{José Machado}%
\author[1]{Carla Rodrigues}%
\author[1]{Luis Mendes Gomes}%
\affil[1]{Universidade do Minho Centro ALGORITMI}%


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  \date{May 24, 2024}


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\begin{abstract}
\textbf{Purpose:} Poly-medicated patients, especially those over 65,
have increased. Multiple drug use and inappropriate prescribing increase
drug-drug interactions, adverse drug reactions, morbidity, and
mortality. This issue was addressed with several CDSS alerts. Health
professionals have not followed these systems due to their poor alert
quality and incomplete databases. \textbf{Methods:} Recent research
shows a growing interest in using Text Mining via NLP to extract
drug-drug interactions from unstructured data sources to support
clinical prescribing decisions. NLP text mining and machine learning
classifier training for drug relation extraction were used in this
process. \textbf{Results:} In this context, the proposed solution allows
to develop an extraction system for drug-drug interactions from
unstructured data sources. The system produces structured information,
which can be inserted into a database that contains information acquired
from three different data sources. \textbf{Conclusion:} The architecture
outlined for the drug-drug interaction extraction system is capable of
receiving unstructured text, identifying drug entities sentence by
sentence, and determining whether or not there are interactions between
them.%
\end{abstract}%



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