BACKGROUND: An accurate medication history, foundational for providing quality medical care, requires understanding of medication change events documented in clinical notes. However, extracting medication changes without the necessary clinical context is insufficient for real-world applications. METHODS: To address this need, Track 1 of the 2022 National NLP Clinical Challenges focused on extracting the context for medication changes documented in clinical notes using the Contextualized Medication Event Dataset. Track 1 consisted of 3 subtasks: extracting medication mentions from clinical notes (NER), determining whether a medication change is being discussed (Event), and determining the action, negation, temporality, certainty, and actor for any change events (Context). Participants were allowed to participate in any one or more of the subtasks. RESULTS: A total of 32 teams with participants from 19 countries submitted a total of 211 systems across all subtasks. Most teams formulated NER as a token classification task and Event and Context as multi-class classification tasks, using transformer-based large language models. Overall, performance for NER was high across submitted systems. However, performance for Event and Context were much lower, often due to indirectly stated change events with no clear action verb, events requiring farther textual clues for understanding, and medication mentions with multiple change events. CONCLUSIONS: This shared task showed that while NLP research on medication extraction is relatively mature, understanding of contextual information surrounding medication events in clinical notes is still an open problem requiring further research to achieve the end goal of supporting real-world clinical applications.

This paper describes a method to identify events being vigorously discussed on social media and to present them in the form of a web-based daily magazine. Tweet texts and hyperlinked information sources, such as images and news articles, are analyzed to discover the events. The events are selected for presentation using an "interestingness factor", which combines several facets of the discussions surrounding the events. The events are correlated based on their content similarity.

Twitter has emerged as a great source to provide insights about upcoming planned and unplanned events of social, economic and political relevance. Big events are publicized and known in advance, but smaller, unplanned sub-events around them are not always advertised. These unplanned events may have a large localized impact. If known in advance, knowledge about events like threats, protests, demonstrations etc. or even about large flash mobs can be utilized by planners and event managers. Given the large volumes of tweets floating around at any given time, identifying relevant sub-events is a non-trivial task. In this paper, we explore machine learning techniques to identify, extract and build a map of small sub-events around a big, popular event. We use CRFs to extract event components from tweets. Events are resolved for uniqueness and compiled into a complete calendar. The model is evaluated on tweets around Olympic Games. The framework is generic enough to be adapted to other domains.