Rapid Review on Publicly Available Datasets for Health Misinformation Detection.

The proliferation of health misinformation in recent years has prompted the development of various methods for detecting and combatting this issue. This review aims to provide an overview of the implementation strategies and characteristics of publicly available datasets that can be used for health misinformation detection. Since 2020, a large number of such datasets have emerged, half of which are focused on COVID-19. Most of the datasets are based on fact-checkable websites, while only a few are annotated by experts. Furthermore, some datasets provide additional information such as social engagement and explanations, which can be utilized to study the spread of misinformation. Overall, these datasets offer a valuable resource for researchers working to combat the spread and consequences of health misinformation.

Multilabel classification of medical concepts for patient clinical profile identification.

BACKGROUND: The development of electronic health records has provided a large volume of unstructured biomedical information. Extracting patient characteristics from these data has become a major challenge, especially in languages other than English. METHODS: Inspired by the French Text Mining Challenge (DEFT 2021) [1] in which we participated, our study proposes a multilabel classification of clinical narratives, allowing us to automatically extract the main features of a patient report. Our system is an end-to-end pipeline from raw text to labels with two main steps: named entity recognition and multilabel classification. Both steps are based on a neural network architecture based on transformers. To train our final classifier, we extended the dataset with all English and French Unified Medical Language System (UMLS) vocabularies related to human diseases. We focus our study on the multilingualism of training resources and models, with experiments combining French and English in different ways (multilingual embeddings or translation). RESULTS: We obtained an overall average micro-F1 score of 0.811 for the multilingual version, 0.807 for the French-only version and 0.797 for the translated version. CONCLUSION: Our study proposes an original multilabel classification of French clinical notes for patient phenotyping. We show that a multilingual algorithm trained on annotated real clinical notes and UMLS vocabularies leads to the best results.