第七届国际健康文本挖掘和信息分析研讨会 7th International Workshop on Health Text Mining and Information Analysis

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活动简介

The Seventh International Workshop on Health Text Mining and Information Analysis provides an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. It started in 2008 in Turku, Finland and has been organized five times: LOUHI 2010 was co-located with NAACL in Los Angeles, CA; LOUHI 2011 was co-located with Artificial Intelligence in Medicine (AIME) in Bled, Slovenia; LOUHI 2013 was held in Sydney, Australia during NICTA Techfest; LOUHI 2014 was co-located with EACL in Gothenburg, Sweden; and LOUHI 2015 was co-located with EMNLP in Lisbon, Portugal. LOUHI 2016 is soliciting papers describing original research. Papers must describe substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress.

征稿信息

重要日期

2016-08-05

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征稿范围

Techniques supporting information extraction, e.g. named entity recognition, negation and uncertainty detection
Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets)
Text representation, including dealing with data sparsity and dimensionality issues
Domain adaptation, e.g. adaptation of standard NLP tools (incl. tokenizers, PoS-taggers, etc) to the medical domain
Information fusion, i.e. integrating data from various sources, e.g. structured and narrative documentation
Unsupervised methods, including distributional semantics
Evaluation, gold/reference standard construction and annotation
Syntactic, semantic and pragmatic analysis of health documents
Anonymization / de-identification of health records and ethics
Supporting the development of medical terminologies and ontologies
Individualization of content, consumer health vocabularies, summarization and simplification of text
NLP for supporting documentation and decision making practices
Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infections