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.
2022
Pereira, P. J.; Costa, N.; Barros, M.; Cortez, P.; Durães, D.; Silva, A.; Machado, J.
A Comparison of Automated Time Series Forecasting Tools for Smart Cities Proceedings Article
Em: G., Paiva A. Martins B. Marreiros (Ed.): pp. 551-562, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109).
Resumo | Links | BibTeX | Etiquetas: Automated machine learning; Automated machines; City traffic; Energy-consumption; Forecasting tools; Key Issues; Machine-learning; Rolling window; Time series forecasting; Times series, Automation; Energy utilization; Forecasting; Smart city; Time series, Machine learning
@inproceedings{Pereira2022551,
title = {A Comparison of Automated Time Series Forecasting Tools for Smart Cities},
author = {P. J. Pereira and N. Costa and M. Barros and P. Cortez and D. Durães and A. Silva and J. Machado},
editor = {Paiva A. Martins B. Marreiros G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138674457&doi=10.1007%2f978-3-031-16474-3_45&partnerID=40&md5=cc3bc2f19869e8c4a50d3a836654d054},
doi = {10.1007/978-3-031-16474-3_45},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13566 LNAI},
pages = {551-562},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 2; Conference of 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference Date: 31 August 2022 Through 2 September 2022; Conference Code:283109},
keywords = {Automated machine learning; Automated machines; City traffic; Energy-consumption; Forecasting tools; Key Issues; Machine-learning; Rolling window; Time series forecasting; Times series, Automation; Energy utilization; Forecasting; Smart city; Time series, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.