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Overview of the ShARe/CLEF eHealth Evaluation Lab 2014
TLDR
The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients.
Overview of the ShARe/CLEF eHealth Evaluation Lab 2013
TLDR
An evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition.
Extending the NegEx Lexicon for Multiple Languages
TLDR
An existing English negation lexicon (NegEx) is translated to Swedish, French, and German and compared the lexicon on corpora from each language, observing Zipf's law for all languages.
Characterisation of mental health conditions in social media using Informed Deep Learning
TLDR
This study analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes, which could automatically recognise mental illness-related posts in the balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%.
Task 1: ShARe/CLEF eHealth Evaluation Lab 2013
TLDR
This task focused on identification and normalization of diseases and disorders in clinical reports using annotations from the ShARe corpus and made the text corpora, annotations, and evaluation tools available for future research and development.
The language of mental health problems in social media
TLDR
The language of Reddit posts specific to mental health is investigated, to define linguistic characteristics that could be helpful for further applications and to demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders.
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.
TLDR
There has been an increase of advances within key NLP subtasks that support semantic analysis, and a reflection upon most recent developments and potential areas of future NLP development and applications is provided.
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing
TLDR
Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database.
HEALTH BANK - A Workbench for Data Science Applications in Healthcare
TLDR
A number of data science applications that have been developed using the Stockholm EPR Corpus database are described, demonstrating the potential reuse of EHR data to support healthcare and public health activities, as well as facilitate medical re- search.
Don't Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records
TLDR
This paper develops and proposes a negation detection method that leverages syntactic features of text 1 and builds a set of basic rules that rely on minimum domain knowledge and render the problem as binary classification (affirmed vs. negated).
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