Social networks have become information dissemination channels, where announcements are posted frequently; they also serve as frameworks for debates in various areas (e.g., scientific, political, and social). In particular, in the health area, social networks represent a channel to communicate and disseminate novel treatments’ success; they also allow ordinary people to express their concerns about a disease or disorder. The Artificial Intelligence (AI) community has developed analytical methods to uncover and predict patterns from posts that enable it to explain news about a particular topic, e.g., mental disorders expressed as eating disorders or depression. Albeit potentially rich while expressing an idea or concern, posts are presented as short texts, preventing, thus, AI models from accurately encoding these posts’ contextual knowledge. We propose a hybrid approach where knowledge encoded in community-maintained knowledge graphs (e.g., Wikidata) is combined with deep learning to categorize social media posts using existing classification models. The proposed approach resorts to state-of-the-art named entity recognizers and linkers (e.g., Falcon 2.0) to extract entities in short posts and link them to concepts in knowledge graphs. Then, knowledge graph embeddings (KGEs) are utilized to compute latent representations of the extracted entities, which result in vector representations of the posts that encode these entities’ contextual knowledge extracted from the knowledge graphs. These KGEs are combined with contextualized word embeddings (e.g., BERT) to generate a context-based representation of the posts that empower prediction models. We apply our proposed approach in the health domain to detect whether a publication is related to an eating disorder (e.g., anorexia or bulimia) and uncover concepts within the discourse that could help healthcare providers diagnose this type of mental disorder. We evaluate our approach on a dataset of 2,000 tweets about eating disorders. Our experimental results suggest that combining contextual knowledge encoded in word embeddings with the one built from knowledge graphs increases the reliability of the predictive models. The ambition is that the proposed method can support health domain experts in discovering patterns that may forecast a mental disorder, enhancing early detection and more precise diagnosis towards personalized medicine.
A dor patelofemoral, também denominada dor anterior do joelho está presente em 25% da população, onde 36% são adolescentes e com maior prevalência no sexo feminino e atletas. Objetivo: Verificar a associação entre a presença de retropé varo a partir da posição neutra da subtalar e a dor patelofemoral. Métodos: Foram recrutados 10 voluntários com dor patelofemoral unilateral ou bilateral. Os voluntários foram submetidos à avaliação do alinhamento do retropé a partir da posição neutra da subtalar. Para isso, os voluntários foram posicionados em decúbito ventral, com o pé pendente para fora da mesa. Os ângulos formado pelas retas que dividem as pernas e os calcâneos ao meio foram medidas através de um goniômetro universal. O teste de Fisher foi utilizado para verificar a associação entre dor patelofemoral e varismo de retropé maior ou igual ou menor que 8 graus. Resultados: Os resultados do presente estudo demonstraram que todos os membros com grau de retropé maior que 8 (75%) apresentavam dor, totalizando 15 joelhos. Já os joelhos avaliados com retropé menor ou igual a 8; 2 (10%) apresentavam dor e 3 (15%) não apresentaram dor. Associação estatisticamente significativa entre o grau de retropé e a presença de dor foram encontrados (p= 0,009). Discussão: O varismo de retropé leva a pronação excessiva da subtalar associada à rotação interna da tíbia com conseqüente alteração do alinhamento do membro inferior e dor patelofemoral, Conclusão: Os resultados desse estudo sugerem que existe a associação entre o retropé varo e a dor patelofemoral.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.