10th International Workshop on Health Text Mining and Information Analysis

Страна: Гонконг

Город: Hong Kong

Тезисы до: 12.08.2019

Даты: 03.11.19 — 04.11.19

Е-мейл Оргкомитета: https://www.softconf.com/emnlp2019/ws-LOUHI2019/

Организаторы: LOUHI Committee


The Tenth 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 textual content of 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 9 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; LOUHI 2015 was co-located with EMNLP in Lisbon, Portugal; LOUHI 2016 was co-located with EMNLP in Austin, Texas; LOUHI 2017 was held in Sydney, Australia; and LOUHI 2018 was co-located with EMNLP in Brussels, Belgium.

LOUHI 2019 is soliciting papers describing original research. Papers must describe substantial and completed work but could also focus on a contribution, a negative result, a software package or work in progress. The topics include, but are not limited to, the following language processing techniques and related areas:

- 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

We welcome submissions on topics related to text mining of health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. We also encourage submissions reporting work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity.

Веб-сайт конференции: https://linguistlist.org/issues/30/30-1931.html