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Background: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches., Methods: The data was extracted from Twitter based on pre-defined keywords (i.e. 'hayfever' OR 'hay fever') throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed., Results: The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions)., Conclusions: The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of 'black-box' approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.

Background: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.Methods: We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.Results: Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.Conclusions: BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions. [ABSTRACT FROM AUTHOR]

Background: Stigma surrounding substance use can result in severe consequences for physical and mental health. Identifying situations in which stigma occurs and characterizing its impact could be a critical step toward improving outcomes for individuals experiencing stigma. As part of a larger research project with the goal of informing the development of interventions for substance use disorder, this study leverages natural language processing methods and a theory-informed approach to identify and characterize manifestations of substance use stigma in social media data. Methods: We harvested social media data, creating an annotated corpus of 2,214 Reddit posts from subreddits relating to substance use. We trained a set of binary classifiers; each classifier detected one of three stigma types: Internalized Stigma, Anticipated Stigma, and Enacted Stigma, from the Stigma Framework. We evaluated hybrid models that combine contextual embeddings with features derived from extant lexicons and handcrafted lexicons based on stigma theory, and assessed the performance of these models. Then, using the trained and evaluated classifiers, we performed a mixed-methods analysis to quantify the presence and type of stigma in a corpus of 161,448 unprocessed posts derived from subreddits relating to substance use. Results: For all stigma types, we identified hybrid models (RoBERTa combined with handcrafted stigma features) that significantly outperformed RoBERTa-only baselines. In the model's predictions on our unseen data, we observed that Internalized Stigma was the most prevalent stigma type for alcohol and cannabis, but in the case of opioids, Anticipated Stigma was the most frequent. Feature analysis indicated that language conveying Internalized Stigma was predominantly characterized by emotional content, with a focus on shame, self-blame, and despair. In contrast, Enacted Stigma and Anticipated involved a complex interplay of emotional, social, and behavioral features. Conclusion: Our main contributions are demonstrating a theory-based approach to extracting and comparing different types of stigma in a social media dataset, and employing patterns in word usage to explore and characterize its manifestations. The insights from this study highlight the need to consider the impacts of stigma differently by mechanism (internalized, anticipated, and enacted), and enhance our current understandings of how each stigma mechanism manifests within language in particular cognitive, emotional, social, and behavioral aspects. [ABSTRACT FROM AUTHOR]

Background: Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma and fear of cancer recurrence. The widespread use of Twitter for socializing has been the alternative medium for data collection compared to traditional studies of mental health, which primarily depend on information taken from medical staff with their consent. These social media data, to a certain extent, reflect the users' psychological state. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming.Methods: We stream the data using cancer as a keyword to filter the tweets with cancer-free and use post-traumatic stress disorder (PTSD) related keywords to reduce the time spent on the annotation task. Convolutional Neural Network (CNN) learns the representations of the input to identify cancer survivors with PTSD.Results: The results present that the proposed CNN can effectively identify cancer survivors with PTSD. The experiments on real-world datasets show that our model outperforms the baselines and correctly classifies the new tweets.Conclusions: PTSD is one of the severe anxiety disorders that could affect individuals who are exposed to traumatic events, including cancer. Cancer survivors are at risk of short-term or long-term effects on physical and psycho-social well-being. Therefore, the evaluation and treatment of PTSD are essential parts of cancer survivorship care. It will act as an alarming system by detecting the PTSD presence based on users' postings on Twitter. [ABSTRACT FROM AUTHOR]