Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning

Kumar, Adarsh; Kamal, Ojasv; Mazumdar, Susmita

doi:10.18653/v1/2021.smm4h-1.22

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Leveraging the power of these techniques can provide valuable insights and assist in the early detection and treatment of breast cancer. Kumar et al [102] tailored a BERT-based model to specifically address the classification of breast cancer-related posts on Twitter, as described in Shared Task 8 of SMM4H-2021. Their approach was to employ BlueBERT [103], which is pre-trained on a comprehensive biomedical corpus acquired from PubMed, enhancing the model's understanding of medical terminology and context.…”

Section: Diagnosis Of Breast Cancermentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

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Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

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Section: Diagnosis Of Breast Cancermentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

Magge¹,

Klein²,

Miranda-Escalada³

et al. 2021

Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

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The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health. The Social Media Mining for Health Research and Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multiclass classification, named entity recognition (NER) and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.

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Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning

Cited by 2 publications

References 6 publications

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021

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