Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.
J Am Med Inform Assoc
; 31(4): 991-996, 2024 04 03.
Article
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| MEDLINE
| ID: mdl-38218723
ABSTRACT
OBJECTIVE:
The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.METHODS:
The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events).RESULTS:
In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora.CONCLUSION:
To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.Palabras clave
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Base de datos:
MEDLINE
Asunto principal:
Medios de Comunicación Sociales
Idioma:
En
Revista:
J Am Med Inform Assoc
/
J. am. med. inform. assoc
/
Journal of the american medical informatics association
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article