Organized in conjunction with IEEE ICHI 2018
June 7th, 2018, New York City, USA
Mental health is an increasingly important problem in healthcare. Based on the data from the 2015 National Survey on Drug Use and Health (NSDUH), 1 in 5 adults experience a mental illness, and nearly 1 in 25 adults lives with a serious mental illness in the United States. Especially, the annual suicide rate in the U.S. has continued to climb over the past several decades and suicide is the 10th leading causes of death in the U.S. The economic impact of suicidal behavior has been estimated to exceed more than $51 billion annually in the U.S.
In recent years, there has been a rapid growth in the implementation of electronic health records (EHRs), leading to an unprecedented expansion in the availability of dense longitudinal datasets for clinical and translational research for psychiatric disorders. Meanwhile, the rapidly increasing, huge archive of consumer data in social media such as Twitter and Facebook also provides unprecedented opportunities to access a broad population with the mental health issues and suicidal behavior. 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, it is very important to extract risk factors, phenotyping information, and human behaviors automatically from EHRs and social media data. Moreover, the extracted information needs to be formally represented in an ontological semantic framework for further applications and reasoning. However, psychiatric information often shows 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 the challenges.
The goal of this workshop is to bring experts in the field of natural language processing, knowledge representation, knowledge management, and health data analytics to discuss innovative analytical methods, applications, and tools to address problems in mental health.
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 2018 and published in the IEEE ICHI 2018 Proceedings (in IEEE Xplore Digital Library); Selected FULL/SHORT papers will be invited to publish an extended version in the supplement of Health Informatics Journal (IF 3.021). Selected high-quality SHORT papers will also be invited to submit an extended version of the journal supplement for consideration.
Note: If a paper is selected for possible journal publication, the authors will be asked to shorten their workshop paper to be published in the ICHI 2018 Proceedings and then submit the journal version after the workshop. The authors can still choose to publish their full papers in the conference proceedings, in which case, however, the authors will NOT be eligible to publish in the journal supplement due to the journal's self-plagiarism concern.
We are inviting original research submissions as well as work-in-progress.
Topics of interest include but not limited to:
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Deadline for paper submission |
April 12th, 2018 |
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Notification of acceptance |
April 27th, 2018 |
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Camera-ready |
May 14th, 2018 |
Jessie Tenenbaum and Piper Ranallo
Title: Mental Health Informatics and Knowledge Representation: a report from the American Medical Informatics Association's Working Group on Mental Health Informatics
Abstract:
The Mental Health Informatics Working Group within the American Medical Informatics Association (AMIA) was founded in 2016 to facilitate communication, collaboration, education, and networking among researchers and practitioners working at the interface of informatics and mental health, including substance use, in order to better understand and improve mental health and healthcare delivery. A number of subgroups within the Working Group focus on specific project areas including natural language processing, biomarker discovery, and data standards and terminologies. The Data Standards and Terminologies subgroup focuses on knowledge representation and has established collaborations with national and international standards organizations including HL7, SNOMED International, the American Psychiatric Association, and the National Institute of Mental Health RDoC Unit to facilitate the extension and enhancement of existing data standards and terminologies to better support both mental health research and clinical care. This talk will describe both high level working group activities and specific semantics-related initiatives, as well as how to get involved with the group.
Dr. Tenenbaum is a faculty member at Duke University’s School of Medicine. After 8 years as Associate Director for Bioinformatics for the Duke Translational Research Institute, Dr. Tenenbaum joined the Division of Translational Informatics within the Department of Biostatistics and Bioinformatics in 2015. Her research applies expertise in data standards and electronic health records to stratify mental health disorders to enable precision medicine. She is also the informatics faculty lead for the Alzheimer's Disease Metabolomics Consortium. Nationally, Dr. Tenenbaum is a member of the Board of Directors for the American Medical Informatics Association (AMIA) and serves on the Board of Scientific Counselors for the Lister Hill Center at the National Library of Medicine. She is co-founder and Chair of AMIA's Mental Health Informatics Discussion Forum and Past Chair of AMIA’s Genomics and Translational Bioinformatics Working Group. She is an Associate Editor for the Journal of Biomedical Informatics and serves on the advisory panel for Nature Scientific Data. After earning her bachelor’s degree in biology from Harvard, Dr. Tenenbaum worked as a program manager at Microsoft Corporation in Redmond, WA for six years before pursuing a
Piper Ranallo,
Please feel free to contact us at yaoyun.zhang@uth.tmc.edu, if you have any questions.