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Drug-Drug Interaction Extraction-Based System: an NLP Approach
  • +1
  • Regina Sousa,
  • José Machado,
  • Carla Rodrigues,
  • Luis Mendes Gomes
Regina Sousa
Universidade do Minho Centro ALGORITMI
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José Machado
Universidade do Minho Centro ALGORITMI

Corresponding Author:jmac@di.uminho.pt

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Carla Rodrigues
Universidade do Minho Centro ALGORITMI
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Luis Mendes Gomes
Universidade do Minho Centro ALGORITMI
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Abstract

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. 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. 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. 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.
28 Feb 2023Submitted to Expert Systems
01 Mar 2023Submission Checks Completed
01 Mar 2023Assigned to Editor
03 Mar 2023Reviewer(s) Assigned
13 Mar 2023Review(s) Completed, Editorial Evaluation Pending
13 Mar 2023Editorial Decision: Revise Minor
20 Mar 20231st Revision Received
21 Mar 2023Submission Checks Completed
21 Mar 2023Assigned to Editor
22 Mar 2023Reviewer(s) Assigned
24 Mar 2023Review(s) Completed, Editorial Evaluation Pending
24 Mar 2023Editorial Decision: Accept