Information Extraction
2013 | Named Entity Recognition in English clinical reports The clinical narrative is abundant in mentions of clinical conditions, anatomical sites, medications, and procedures. This tasks consists of (a) discovering the mention boundaries and (b) mapping each mention to a UMLS CUI Details |
Normalization of acronyms/abbreviations Many of the terms found in clinical documents are acronyms or abbreviations that can be difficult for patients to understand. This task consists of mapping pre-annotated acronym/abbreviation mention to UMLS CUIs. |
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2014 | Information Extraction from Clinical Text For this task, participants will be provided an empty template for each disease/disorder mention; each template consists of the mention’s UMLS CUI, mention boundaries, and unfilled attribute: value slots. Participants are asked to develop attribute classifiers that predict the value for each each attribute:value slot for the provided disease/disorder mention. Details |
2015 | Clinical Named Entity Recognition The CLEFeHealth 2015 Task 1b addresses clinical named entity recognition in languages other than English. The aim is to automatically identify clinically relevant entities in medical text in French. Details |
2016 | Clinical Named Entity Recognition The clinical narrative is abundant in mentions of clinical conditions, anatomical sites, medications, and procedures. This tasks consists of (a) discovering the mention boundaries and (b) mapping each mention to a UMLS CUI Details |
Information Extraction from Death Deports Many of the terms found in clinical documents are acronyms or abbreviations that can be difficult for patients to understand. This task consists of mapping pre-annotated acronym/abbreviation mention to UMLS CUIs.Participants are challenged with the extraction of causes of death from a new corpus of French death reports. This task can naturally be treated as a named entity recognition and normalization task, but also as a text classification task. Details |
Information Management
2014 | Visual-Interactive Search and Exploration of eHealth Data The task challenges participants to design interactive visualisations that help patients better understand their discharge summaries and explore additional relevant documents in light of a large document corpus and their various facets in context. Details |
2015 | Clinical Speech Recognition The aim is to convert verbal nursing handover to written free-text records. We challenge the participants to minimize word-detection errors by: (1) addressing the correctness of the speech recognition engine itself and/or (2) improving this through post-processing methods for the recognised text. Details |
2016 | Handover Information Extraction This task addresses clinical information extraction related to Australian nursing shift changes. This extends the 2015 task of converting verbal nursing handover to written free-text records; we challenge participants to maximise the correctness in structuring these written free-text records by pre-filling a handover form by automatically identifying relevant text-snippets for each slot of the form. Details |
Information Retrieval
2013-2016 | Patient-centered Information Retrieval This task aims to evaluate the effectiveness of information retrieval systems when searching for health content on the web, with the objective to foster research and development of search engines tailored to health information seeking. Details:2013 2014 2015 2016 |
2014-2016 | Cross-lingual Information Retrieval As a complementary task to the Patient-centered Information Retrieval task, this task aims at develop multilingual approaches, in order to retrieve English documents relevant to non-English queries Details:2014 2015 2016 |
2016 | Interactive Search This task explores iterative query reformulation along with the associated document retrieval, with the aim of studying differences between querying strategies and how these are supported by the developed systems. Details |
Link to the original page https://sites.google.com/site/clefehealth/tasks