Thailand

This study describes the approach of Team ADE Oracle for Task 1 of the Social Media Mining for Health Applications (#SMM4H) 2024 shared task. Task 1 challenges partic- ipants to detect adverse drug events (ADEs) within English tweets and normalize these men- tions against the Medical Dictionary for Regu- latory Activities standards. Our approach uti- lized a two-stage NLP pipeline consisting of a named entity recognition model, retrained to recognize ADEs, followed by vector similar- ity assessment with a RoBERTa-based model. Despite achieving a relatively high recall of 37.4% in the extraction of ADEs, indicative of effective identification of potential ADEs, our model encountered challenges with preci- sion. We found marked discrepancies between recall and precision between the test set and our validation set, which underscores the need for further efforts to prevent overfitting and en- hance the model’s generalization capabilities for practical applications.

ACL 2024

ADE Oracle at #SMM4H 2024: A Two-Stage NLP System for Extracting and Normalizing Adverse Drug Events from Tweets

social media text mining & analytics in public health

spacy

meddra normalization

adverse drug event (ade) detection

computational linguistics (cl)

named entity recognition (ner)

natural language processing (nlp)

machine learning (ml)

pharmacovigilance

roberta

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

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The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iREL’s approach to the SMM4H 2024 Shared Task, leveraging the inherent topi- cal information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of anno- tated data.

BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets

We describe the methods and results of our submission to the 9th Social Media Mining for Health Research and Applications (SMM4H) 2024 shared tasks 4 and 5. Task 4 involved extracting the clinical and social impacts of non-medical substance use and task 5 focused on the binary classification of tweets reporting children’s medical disorders. We employed encoder language models and their ensembles, achieving the top score on task 4 and a high score for task 5.

UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5)

Adverse drug events (ADEs) pose major pub- lic health risks, with traditional reporting sys- tems often failing to capture them. Our pro- posed pipeline, called Deep-LLMADEminer, used natural language processing approaches to tackle this issue for #SMM4H 2024 shared task 1. Using annotated tweets, we built a three part pipeline: RoBERTa for classification, GPT- 4-turbo for span extraction, and BioBERT for normalization. Our models achieved F1-scores of 0.838, 0.306, and 0.354, respectively, of- fering a novel system for Task 1 and similar pharmacovigilance tasks.

LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter

This paper describes the work undertaken as part of the SMM4H-2024 shared task, specifi- cally Task 5, which involves the binary classifi- cation of English tweets reporting children’s medical disorders. The primary objective is to develop a system capable of automat- ically identifying tweets from users who re- port their pregnancy and mention children with specific medical conditions, such as attention- deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma, while distinguishing them from tweets that merely reference a disorder without much context. Our approach leverages advanced nat- ural language processing techniques and ma- chine learning algorithms to accurately classify the tweets. The system achieved an overall F1- score of 0.87, highlighting its robustness and effectiveness in addressing the classification challenge posed by this task.

HaleLab_NITK@SMM4H’24: Binary Classification of English Tweets reporting Children’s Medical Disorders

This paper explores the potential of social me- dia as a rich source of data for understanding public health trends and behaviors, particularly focusing on emotional well-being and the im- pact of environmental factors. We employed large language models (LLMs) and developed a suite of knowledge extension techniques to analyze social media content related to men- tal health issues, specifically examining 1) ef- fects of outdoor spaces on social anxiety symp- toms in Reddit, 2) tweets reporting children’s medical disorders, and 3) self-reported ages in posts of Twitter and Reddit. Our knowl- edge extension approach encompasses both su- pervised data (i.e., sample augmentation and cross-task fine-tuning) and unsupervised data (i.e., knowledge distillation and cross-task pre- training), tackling the inherent challenges of sample imbalance and informality of social media language. The effectiveness of our ap- proach is demonstrated by the superior perfor- mance across multiple tasks (i.e., Task 3, 5 and 6) at the SMM4H-2024. Notably, we achieved the best performance in all three tasks, under- scoring the utility of our models in real-world applications.

CTYUN-AI@SMM4H-2024: Knowledge Extension Makes Expert Models

This paper presents our models for the Social Media Mining for Health 2024 shared task, specifically Task 5, which involves classifying tweets reporting a child with childhood dis- orders (annotated as "1") versus those merely mentioning a disorder (annotated as "0"). We utilized a classification model enhanced with diverse textual and language model-based aug- mentations. To ensure quality, we used seman- tic similarity, perplexity, and lexical diversity as evaluation metrics. Combining supervised con- trastive learning and cross-entropy-based learn- ing, our best model, incorporating R-drop and various LM generation-based augmentations, achieved an impressive F1 score of 0.9230 on the test set, surpassing the task mean and me- dian scores.

KUL@SMM4H2024: Optimizing Text Classification with Quality-Assured Augmentation Strategies

This paper summarizes our participation in the Shared Task 4 of #SMM4H 2024. Task 4 was a named entity recognition (NER) task identify- ing clinical and social impacts of non-medical substance use in English Reddit posts. We em- ployed the Bidirectional Encoder Representa- tions from Transformers (BERT) model to com- plete this task. Our team achieved an F1-score of 0.892 on a validation set and a relaxed F1- score of 0.191 on the test set.

LHS712NV at #SMM4H 2024 Task 4: Using BERT to classify Reddit posts on non-medical substance use

The goal of Social Media Mining for Health (#SMM4H) 2024 Task 7 was to train a machine learning model that is able to distinguish between annotations made by humans and those made by a Large Language Model (LLM). The dataset consisted of tweets originating from #SMM4H 2023 Task 3, wherein the objective was to extract COVID-19 symptoms in Latin- American Spanish tweets. Due to the lack of additional annotated tweets for classification, we reframed the task using the available tweets and their corresponding human or machine annotator labels to explore differences between the two subsets of tweets. We conducted an exploratory data analysis and trained a BERT-based classifier to identify sampling biases between the two subsets. The exploratory data analysis found no significant differences between the samples and our best classifier achieved a precision of 0.52 and a recall of 0.51, indicating near-random performance. This confirms the lack of sampling biases between the two sets of tweets and is thus a valid dataset for a task designed to assess the authorship of annotations by humans versus machines.

712forTask7 at #SMM4H 2024 Task 7: Classifying Spanish Tweets Annotated by Humans versus Machines with BETO Models

SMM4H 2024 Task 1 is focused on the identification and standardization of Adverse Drug Events (ADEs) in tweets. We introduce a novel Retrieval-Augmented Generation (RAG) method, leveraging the capabilities of Llama 3, GPT-4, and the SFR-embedding-mistral model, along with few-shot prompting techniques, to map colloquial tweet language to MedDRA Preferred Terms (PTs) without relying on extensive training datasets. Our method achieved competitive performance, with an F1 score of 0.359 in the normalization task and 0.392 in the named entity recognition (NER) task. Notably, our model demonstrated robustness in identifying previously unseen MedDRA PTs (F1=0.363) greatly surpassing the median task score of 0.141 for such terms.

TLab at #SMM4H 2024: Retrieval-Augmented Generation for ADE Extraction and Normalization

In this paper, we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder. For Task 1, our system, composed of a pretrained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.

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BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets

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Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data

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