2023
Machado, J.; Rodrigues, C.; Sousa, R.; Gomes, L. M.
Drug–drug interaction extraction-based system: An natural language processing approach Journal Article
Em: Expert Systems, 2023, ISSN: 02664720, (cited By 2).
Resumo | Links | BibTeX | Etiquetas: Clinical research; Data mining; Drug interactions; Expert systems; Learning algorithms; Natural language processing systems, Data-source; Drug-drug interactions; Information extraction; Interaction extraction; Language processing; Machine-learning; Natural language processing; Natural languages; Text-mining; Unstructured data, Machine learning
@article{Machado2023,
title = {Drug–drug interaction extraction-based system: An natural language processing approach},
author = {J. Machado and C. Rodrigues and R. Sousa and L. M. Gomes},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160305432&doi=10.1111%2fexsy.13303&partnerID=40&md5=3fd3843abb2c89d5d1db4e66c07497e9},
doi = {10.1111/exsy.13303},
issn = {02664720},
year = {2023},
date = {2023-01-01},
journal = {Expert Systems},
publisher = {John Wiley and Sons Inc},
abstract = {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 recommendation systems. Health professionals have not followed these systems due to their poor alert quality and incomplete databases. 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. 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. 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. © 2023 The Authors. Expert Systems published by John Wiley & Sons Ltd.},
note = {cited By 2},
keywords = {Clinical research; Data mining; Drug interactions; Expert systems; Learning algorithms; Natural language processing systems, Data-source; Drug-drug interactions; Information extraction; Interaction extraction; Language processing; Machine-learning; Natural language processing; Natural languages; Text-mining; Unstructured data, Machine learning},
pubstate = {published},
tppubtype = {article}
}
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 recommendation systems. Health professionals have not followed these systems due to their poor alert quality and incomplete databases. 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. 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. 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. © 2023 The Authors. Expert Systems published by John Wiley & Sons Ltd.