CFP: The 2nd International Workshop on the Semantics of Mental Health" to the "Mental Health Informatics Working Group

[Bian,Jiang]

More information at: semantics-powered.org/SemanticMH-2019

 

Mental health is an increasingly important topic in healthcare. Based on the data from the 2015 National Survey on Drug Use and Health (NSDUH), in the United States 1 in 5 adults experience a mental illness, and nearly 1 in 25 live with a serious mental illness. Mental health issues often lead to serious adverse consequences including suicidality. The annual suicide rate in the US has climbed over the past several decades, such that suicide is now the 10th leading cause of death and is associated with $51 billion in annual economic impacts. Further, the rates for many other mental health disorders such as anxiety, depression without suicidality, bipolar disorder, schizophrenia, and substance use disorders are also alarming. In recent years, there has been a rapid growth in the implementation of electronic health record (EHR) systems, leading to an unprecedented expansion in the availability of dense longitudinal datasets for clinical and translational research including those for psychiatric disorders. Meanwhile, the rapidly increasing, huge archives of consumer data from social media platforms such as Twitter and Facebook also provide unprecedented opportunities to access a broad population with mental health issues and suicidality. The real-time information flow on social media makes it possible to monitor and provide early interventions to potential at-risk users, which is imperative for suicide prevention. Therefore, recent years have witnessed a rapid growth of "big data" studies aiming to extract and study risk factors, phenotyping information, and human behaviors from EHRs and social media data. Nevertheless, these extracted data are rarely standardized and have poor semantic interoperability. These heterogeneous datasets need to be formally represented using an ontological and semantic framework for downstream analyses, applications, and reasoning. However, psychiatric information often shows very unique characteristics, such as subjective descriptions of patient experience and idiosyncratic psychosocial backgrounds, leading to challenges of data sparseness and diversity. Novel natural language processing and ontology technologies are needed to address these challenges.

 

 

We are inviting original research submissions (FULL 8 pages), work-in-progress (SHORT 4 pages), and poster abstracts (2 pages, NEW TYPE).  All the accepted submissions will be presented in MentalHealth 2019 and published in the IEEE ICHI 2019 Proceedings (in IEEE Xplore Digital Library); Selected FULL/SHORT papers will be invited to publish an extended version in a special journal issue (last year's papers were published in the Health Informatics Journal). 

Selected high-quality SHORT papers will also be invited to submit an extended version to the journal supplement for consideration.

 

Paper submission deadline: March 15, 2019

 

Notification of acceptance: April 15, 2019

 

Camera ready submission deadline: May 3, 2019

 

Submission Link: easychair.org/conferences?conf=semanticmh2019

 

We are inviting original research submissions as well as work-in-progress. Topics of interest include but not limited to:

 

 

Natural language processing

* Information extraction and retrieval

* Text mining

* Linguistic resources

* Statistical and knowledge-based methods

* Machine learning for NLP

* Sentiment analysis

 

Ontology

* Ontology development and enrichment

* Semantic harmonization and ontology alignment

* Knowledge representation and reasoning

* Formal approaches to semantics

 

Application based on natural language processing and ontology

* Risk factor detection and predictive modeling for mental disorders, such as hospital readmission and suicide attempts

* Early stage surveillance of mental disorders and suicidal events

* Algorithmic phenotyping and cohort identification​