ArticleJMIR medical informatics2022
Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- AI and machine learning for early identification of eating disorders: a narrative review and Indian contextual insights.Eating and weight disorders : EWD · 2026Review
- MultimodalCNN-PD: a Parkinson's disease diagnostics framework using multimodal convolutional neural network.Frontiers in aging neuroscience · 2026Article
- A machine learning language model approach to evaluating mental health awareness content across Spanish- and English-language social media posts on Twitter.Social psychiatry and psychiatric epidemiology · 2025Article
- MDNCT: a multi-domain neurocognitive transformer architecture approach for early prediction of autism spectrum disorders.Scientific reports · 2025Article
- Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis.Journal of medical Internet research · 2025Article
- Transformer-based heart language model with electrocardiogram annotations.Scientific reports · 2025Article
- Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach.PeerJ. Computer science · 2025Article
- Analyzing student mental health with RoBERTa-Large: a sentiment analysis and data analytics approach.Frontiers in big data · 2025Article
- Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing.Health information science and systems · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Optimizing classification of diseases through language model analysis of symptoms.Scientific reports · 2024Article
- Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys.Annals of occupational and environmental medicine · 2024Article
- Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review.Frontiers in psychiatry · 2024Review
- Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.Frontiers in public health · 2024Article
- Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis.Journal of medical Internet research · 2023Article
- Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study.JMIR infodemiology · 2023Article
- Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.Yearbook of medical informatics · 2023Article
- The research foundation for COVID-19 vaccine development.Frontiers in research metrics and analytics · 2023Article
- An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach.JMIR medical informatics · 2022Article
- Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions.Journal of eating disorders · 2022Review
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5 authors.
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Abstract
backgroundEating disorders affect an increasing number of people. Social networks provide information that can help.
objectiveWe aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain.
methodsWe collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model.
resultsA total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%).
conclusionsBidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.
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