Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.Comment: Accepted at LREC 202

Embedded implicature in a new interactive paradigm

Previous research on scalar implicature has primarily relied on metalinguistic judgment tasks and found varying rates of such inferences depending on the nature of the task and contextual manipulations. This paper introduces a novel interactive paradigm involving both a production and a comprehension component, thereby fixing a precise conversational context. The main research question is what is reliably communicated by some in this communicative setting, when the quantifier occurs in unembedded positions as well as embedded positions. Our new paradigm involves an action-based task from which participants’ interpretation of utterances can be inferred. It incorporates a game–theoretic design, including a precise model to predict participants’ behaviour in the experimental context. Our study shows that embedded and unembedded implicatures are reliably communicated by some. We propose two cognitive principles which describe what can be left unsaid. In our experimental context, a production strategy based on these principles is more efficient (with equal communicative success and shorter utterances) than a strategy based on literal descriptions

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

International audienceIn this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multilingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero-and few-shot learning based on a multilingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

International audienceIn this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multilingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero-and few-shot learning based on a multilingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community

KEEPHA at n2c2 2022: Track 1

Team KEEPHA 1 submitted results for all three subtasks based on ensembles and inserting position information using different variations of transformer models. We achieve a strict F1 score of 0.95 (lenient F1 is 0.97) in medication extraction, an F1 of 0.94 (micro) and 0.87 (macro) in event classification and a combined F1 of 0.67 in context classification

KEEPHA at n2c2 2022: Track 1

Team KEEPHA 1 submitted results for all three subtasks based on ensembles and inserting position information using different variations of transformer models. We achieve a strict F1 score of 0.95 (lenient F1 is 0.97) in medication extraction, an F1 of 0.94 (micro) and 0.87 (macro) in event classification and a combined F1 of 0.67 in context classification

NTCIR-17 MedNLP-SC Social Media Adverse Drug Event Detection: Subtask Overview

International audienceThis paper presents the Social Media Adverse Drug Event Detection (SM-ADE) subtask as part of the shared task Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP-SC) at NTCIR-17. The SM-ADE subtask aims to identify a set of symptoms caused by a drug, referred to as adverse drug event (ADE) detection, within social media texts in multiple languages, including Japanese, English, French, and German. The competition attracted 26 teams, of which eight submitted official runs for the SM-ADE subtask. We believe this task will be essential to develop core technologies of practical medical applications in the near future.Cet article présente la sous-tâche Social Media Adverse Drug Event Detection (SM-ADE) dans le cadre de la tâche partagée Medical Natural Language Processing for Social Media and Clinical Texts (MedNLP-SC) du NTCIR-17. La sous-tâche SM-ADE vise à identifier un ensemble de symptômes causés par un médicament, appelé détection des événements indésirables liés aux médicaments (ADE), dans les textes des médias sociaux en plusieurs langues, dont le japonais, l'anglais, le français et l'allemand. La compétition a attiré 26 équipes, dont huit ont soumis des courses officielles pour la sous-tâche SM-ADE. Nous pensons que cette tâche sera essentielle pour développer des technologies de base ayant des applications médicales pratiques dans un avenir proche