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Author: Admin | 2025-04-28
Relaxed F1 of 43.2%.pdfbibabsApproaching SMM4H with Merged Models and Multi-task LearningTilia Ellendorff|Lenz Furrer|Nicola Colic|Noëmi Aepli|Fabio RinaldiWe describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.pdfbibabsIdentifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical RepresentationXinyan Zhao|Deahan Yu|V.G.Vinod VydiswaranIdentifying mentions of medical concepts in social media is challenging because of high variability in free text. In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts. The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets. Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.pdfbibabsCorrelating Twitter Language with Community-Level Health OutcomesArno Schneuwly|Ralf Grubenmann|Séverine Rion Logean|Mark Cieliebak|Martin JaggiWe study how language on social media is linked to mortal diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.pdfbibabsAffective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups More Therapeutic than Twitter?Giuliano Tortoreto|Evgeny Stepanov|Alessandra Cervone|Mateusz Dubiel|Giuseppe RiccardiThe increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, on-line support groups (OSGs) can also have negative
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