User:Rammor/sandbox
Artificial intelligence in healthcare
[edit]Current Research
[edit]Drug Interactions
[edit]Improvements in Natural Language Processing led to the development of algorithms to identify drug-drug interactions in medical literature.[1][2][3][4] Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken.[5] To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.[6] Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were.[7] Researchers continue to use this corpus to standardize the measure of the effectiveness of their algorithms.[1][2][4]
Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports.[2][3] Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.[8]
Drug interaction
[edit]Identification
[edit]Physicians are assisted in their avoidance if drug-drug interactions by databases of known interactions. Since 2013, efforts have been made to systematically extract and compile drug-drug interaction data from medical literature using artificial intelligence. The DDIExtraction Challenge 2013 was built by researchers at Carlos III University Madrid to test algorithms in their ability to accurately read medical literature on drug-drug interactions. A corpus of literature concerning drug-drug interactions was assembled to compare the algorithms' results against a human-made list of data. This corpus continues to be used by researchers to test algorithms in against a standard.[1][4]
- ^ a b c B. Bokharaeian and A. Diaz, “Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency,” Journal of Artificial Intelligence and Data Mining, vol. 4, no. 2, pp. 203–212, 2016.
- ^ a b c R. Cai et al., “Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports,” Artificial Intelligence In Medicine, vol. 76, pp. 7–15, 2017.
- ^ a b F. Christopoulou, T. T. Tran, S. K. Sahu, M. Miwa, and S. Ananiadou, “Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.,” J Am Med Inform Assoc, Aug. 2019.
- ^ a b c D. Zhou, L. Miao, and Y. He, “Position-aware deep multi-task learning for drug–drug interaction extraction,” Artificial Intelligence In Medicine, vol. 87, pp. 1–8, 2018.
- ^ García Morillo, J.S. Optimización del tratamiento de enfermos pluripatológicos en atención primaria UCAMI HHUU Virgen del Rocio. Sevilla. Spain. Available for members of SEMI at: ponencias de la II Reunión de Paciente Pluripatológico y Edad Avanzada Archived 2013-04-14 at Archive.today
- ^ M. Herrero-Zazo, I. Segura-Bedmar, P. Martínez, and T. Declerck, “The DDI corpus: An annotated corpus with pharmacological substances and drug–drug interactions,” Journal of Biomedical Informatics, vol. 46, no. 5, pp. 914–920, Oct. 2013.
- ^ I. Segura-Bedmar, P. Martínez, and M. Herrero-Zazo, “SemEval-2013 Task 9: Extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013),” Second Joint Conference on Lexical and Computational Semantics, vol. 2, pp. 341–350, Jun. 2013.
- ^ B. Xu et al., “Incorporating User Generated Content for Drug Drug Interaction Extraction Based on Full Attention Mechanism.,” IEEE Trans Nanobioscience, vol. 18, no. 3, pp. 360–367, Jul. 2019.
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