Image courtesy of Jiang Bian.

Image courtesy of Jiang Bian

Guest Editors: Jiang Bian^a, Cui Tao^b, Yi Guo^a, Jennifer Dahne^c

^aDepartment of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA

^bSchool of Biomedical Informatics, The University of Texas Health Science Center, Houston, Texas, USA.

^cDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.

Contact editor: Jiang Bian; bianjiang@ufl.edu

Overview

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. The rapidly increasing 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.

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. However, 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.

This special issue spawned from the 2nd International Workshop on the Semantics of Mental Health (SemanticMH 2019, http://semantics-powered.org/SemanticMH-2019/), which was successfully organized and hosted in conjunction with the seventh IEEE International Conference on Healthcare Informatics (ICHI 2019) on June 10th, 2019 in Xi’an, China.

Scope

Topics of interest include but are not limited to:

Natural language processing

Ontology

Application based on natural language processing and ontology

Call for papers: We are (1) inviting authors whose papers were accepted in the SemanticsMH 2019 workshop to submit an extended journal version (with significant new content including new experiments and results), and (2) making this an open-call for additional original research submissions.

Submitted papers should report new and original results that are unpublished elsewhere. Please prepare your manuscript with the template file and guidelines found at https://medinform.jmir.org/about/submissions.

Submissions should be sent through the online system at https://medinform.jmir.org/author. Authors should choose the section ‘Theme issue 2019: Semantics of Mental Health’ when submitting papers (see FAQ article on how to submit to a theme issue: https://support.jmir.org/hc/en-us/articles/115001429168-How-do-I-submit-to-a-theme-issue-).

All submitted manuscripts will undergo a full peer review process consistent with the usual rigorous editorial criteria for JMIR. Accepted papers will be published in JMIR Medical Informatics, with all papers appearing together in an e-collection (theme issue) guest edited by the guest editors.

As an open access journal, JMIR Medical Informatics will charge an Article Processing Fee (APF). For this theme issue, the regular APF is discounted by 20%. Please see the fee schedule for details: https://medinform.jmir.org/about/editorialPolicies#custom0

Schedule

Full Paper Due: April 15, 2020

Notification of Acceptance (est.): April 15, 2020

Final Version of Paper Due (est.): June 15, 2020

Special Issue Publication Date (est.): September 1, 2020

Open peer review