Abstract
In the health-related field, drug non-compliance situations happen when patients do not follow their prescriptions and do actions which lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora with information retrieval methods in order to identify messages that contain drug non-compliance information. Exploitation of information retrieval methods permits to detect non-compliance messages with up to 0.529 F-measure, compared to 0.824 F-measure reached with supervized machine learning methods. For some fine-grained categories and on new data, it shows up to 0.70 Precision.
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Acknowledgments
This work has been performed as part of the DRUGSSAFE project funded by the ANSM, France and of the MIAM project funded by the ANR, France within the reference ANR-16-CE23-0012. We thank both programs for their funding. We would like also to thank the annotators who helped us with the manual annotation of misuses, Bruno Thiao Layel for extracting the corpus, Vianney Jouhet and Bruno Thiao Layel for building the list with drugs names, and The-Hien Dao for the set of disorders exploited. Finally, we thank the whole ERIAS team for the discussions.
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Bigeard, S., Thiessard, F., Grabar, N. (2023). Detection and Analysis of Drug Non-compliance in Internet Fora Using Information Retrieval Approaches. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_11
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