Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is curren
tly unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.
We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and the characteristics of contextual word embeddings to extract two views
from documents. Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view. Finally, the initialized classifier in each view is further trained using the initial training examples. We evaluated our model in the largest publicly available ADR dataset. The experiments testify that our model significantly outperforms the transformer-based models pretrained on domain-specific data.
This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERT
weet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.
The paper researches the problem of drug adverse effect detection in texts of social media. We describe the development of such classification system for Russian tweets. To increase the train dataset we apply a couple of augmentation techniques and analyze their effect in comparison with similar systems presented at 2021 years' SMM4H Workshop.
منذ مطلع القرن العشرين بدأ المشرع الدولي يهتم بعقد اتفاقيات دولية لمعالجة مشكلة المخدرات على الصعيد الدولي بعدما أضحت هذه المشكلة مشكلة دولية.
The current research problem sheds light on family socialization and its relation to
drug abuse by presenting the concept of social upbringing in general and family in
particular, and identifying the forms of this formation that may contribute to i
ts erroneous
models in the emergence of many phenomena of deviation such as drug abuse.
The aim of the research is to: Recognize the functions of social upbringing in order to
activate its role in the treatment of phenomena of deviation.
This research has reached a number of results, the most important of which is that the
form of family formation in which the individual is exposed plays an important role in
predicting the normal or abnormal behavior patterns that the individual will exercise in the
future.
This research ended with a number of proposals reached through the theoretical
framework of this research as well as an analysis of the results of previous studies related
to the subject.
Usually, dentists search for methods to provide a painless treatment using local
anesthesia. The aim of the study is to evaluate and compare the anesthetic efficiency of three anesthetics
used in dental practice in Damascus, in both maxillary lateral incisor and first molar.
