Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).

This paper describes approaches and results for shared Task 1 and 4 of SMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english tweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary classification of English Reddit posts self-reporting a social anxiety disorder diagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all participants. We have leveraged the Transformer model (BERT) in combination with the LightGBM model for both tasks.

Task 1 and Task 4 focus on COVID-19 diagnosis in self-reported English tweets and self-reported social anxiety disorder diagnosis posted in Reddit. Task 3 concentrates on detecting and extracting COVID-19 symptoms in Latin American Spanish tweets description. The dataset of Task 1 contain texts from Twitter that self-report the COVID-19 diagnosis (labeled as '1') or not (labeled as '0'). The size of the training set, validation set, and test set are 7600, 400, 10000. Task 4 contains 8117 posts from users aged 12 to 25. Positive cases (labeled as '1') represent self-reported or probable social anxiety disorder diagnoses, while negative cases (labeled as '0') include users without a diagnosis or with uncertain diagnostic status. The sizes of the training set, validation set, and those of test set are 6090, 680, 1347. Task 3 focuses on the detection and extraction of COVID-19 symptoms in tweets written specifically in Latin American Spanish, includes both personal self-reports and third-party mentions of symptoms. There are 6021 of the training data, 1979 for validation, and 2150 for testing.

Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such as BERT. Despite the great number of Transformer-based architectures used in the literature, it is unclear which of them has better performances and why. Therefore, in this paper we perform an extensive evaluation and analysis of 19 Transformer-based models for ADE extraction on informal texts. We compare the performance of all the considered models on two datasets with increasing levels of informality (forums posts and tweets). We also combine the purely Transformer-based models with two commonly-used additional processing layers (CRF and LSTM), and analyze their effect on the models performance. Furthermore, we use a well-established feature importance technique (SHAP) to correlate the performance of the models with a set of features that describe them: model category (AutoEncoding, AutoRegressive, Text-to-Text), pretraining domain, training from scratch, and model size in number of parameters. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data.

The reported work is our straightforward approach for the shared task Classification of tweets self-reporting age organized by the Social Media Mining for Health Applications (SMM4H) workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.

Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning models from the algorithmic perspective to be suitable to run on wearable devices. Hyperdimensional computing (HDC) offers a well-suited on-device learning solution for resource-constrained devices and provides support for privacy-preserving personalization. Our HDC-based method offers flexibility, high efficiency, resilience, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves the energy efficiency of training by up to $45.8\times$ compared with the state-of-the-art Deep Neural Network (DNN) algorithms while offering a comparable accuracy.

Kambhampati, 2020; Etzioni and DeCario, 2020; Vaishya et al., 2020; Wynants and colleagues, 2020; Artificial Intelligence (AI) technologies have long Srivastava, 2020]. Early in the pandemic, authors been positioned as a tool to provide crucial datadriven like [Kambhampati, 2020; Etzioni and DeCario, 2020; decision support to people. In this survey Vaishya et al., 2020] highlighted various scenarios where paper, we look at how AI in general, and collaboration AI could help in tackling COVID19 as well as some of the assistants (CAs or chatbots for short) in particular, potential pitfalls. The AI efforts were helped by different have been used during a true global exigency types of data being freely made available, calls for open - the COVID-19 pandemic. The key observation collaboration [Woodward, 2020] and a sense of urgency. is that chatbots missed their Apollo moment In Table 1, a sample of AI's potential application during when they could have really provided contextual, COVID-19 is shown. They range from decisions to foster personalized, reliable decision support at scale that understanding of the disease and its impact to helping take the state-of-the-art makes possible. We review the actions for individuals, groups and the society at large.

It's impossible to talk about artificial intelligence without mentioning IBM's Watson. A pioneer in cognitive computing, the American computer giant has found multiple health applications for Watson. Pascal Sempé, senior sales consultant for Watson Health Solutions in France, explained how Watson functions and what's at stake. ME e-mag: Could Watson ever replace doctors? Pascal Sempé: Watson is a tool that helps the doctor, certainly not one that tells the doctor what to do.

Amazon has covertly opened a secret lab called '1492' that's dedicated to overhauling healthcare. Headquartered in Seattle, Washington, the skunkworks facility is focused on both hardware and software projects, and is looking into'telemedicine.' Anonymous sources close to Amazon told CNBC the company has become'increasingly interested in exploring new business in healthcare.' Amazon covertly opened a secret lab called '1492' that's dedicated to overhauling healthcare. Headquartered in Seattle, Washington, the skunkworks facility is focused on both hardware and software projects and is looking into'telemedicine' Even the online job posting for the lab are hush-hush. On Amazon's job board, the secret roles are listed as'a1.492'