Scalable mental health analysis in the clinical whitespace via natural language processing

Our increasingly digital life provides a wealth of data about our behavior, beliefs, mood, and well-being. This data provides some insight into the lives of patients outside the healthcare setting, and in aggregate can be insightful for the person's mental health and emotional crisis. Here, we introduce this community to some of the recent advancement in using natural language processing and machine learning to provide insight into mental health of both individuals and populations. We advocate using these linguistic signals as a supplement to those that are collected in the health care system, filling in some of the so-called “whitespace” between visits.

[1]  Eric Horvitz,et al.  Predicting postpartum changes in emotion and behavior via social media , 2013, CHI.

[2]  J. Talbott,et al.  Cross-national prevalence and risk factors for suicidal ideation, plans and attempts , 2009 .

[3]  Bethany J Figg,et al.  Substance Abuse and Mental Health Services Administration , 2018, Journal of Consumer Health on the Internet.

[4]  Mike Conway,et al.  Ethical issues in using Twitter for population-level depression monitoring: a qualitative study , 2016, BMC Medical Ethics.

[5]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[6]  Mark Dredze,et al.  From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses , 2015, CLPsych@HLT-NAACL.

[7]  Cindy K. Chung,et al.  The development and psychometric properties of LIWC2007 , 2007 .

[8]  Mark Dredze,et al.  Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides , 2015, HT.

[9]  Mike Conway.  Ethical Issues in Using Twitter for Public Health Surveillance and Research: Developing a Taxonomy of Ethical Concepts From the Research Literature , 2014, Journal of medical Internet research.

[10]  Nazli Goharian,et al.  Triaging Mental Health Forum Posts , 2016, CLPsych@HLT-NAACL.

[11]  A. Feigl,et al.  The Global Economic Burden of Noncommunicable Diseases , 2012 .

[12]  Maarten Sap,et al.  Towards Assessing Changes in Degree of Depression through Facebook , 2014, CLPsych@ACL.

[13]  Glen A. Coppersmith,et al.  Quantifying Suicidal Ideation via Language Usage on Social Media , 2015 .

[14]  Megha Agrawal,et al.  Characterizing Geographic Variation in Well-Being Using Tweets , 2013, ICWSM.

[15]  Kevin A Padrez,et al.  Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study , 2017, Journal of medical Internet research.

[16]  David R. Williams,et al.  Cross-national prevalence and risk factors for suicidal ideation, plans and attempts , 2008, British Journal of Psychiatry.

[17]  Mark Dredze,et al.  Measuring Post Traumatic Stress Disorder in Twitter , 2014, ICWSM.

[18]  Holly Hedegaard,et al.  Increase in Suicide in the United States, 1999-2014. , 2016, NCHS data brief.

[19]  Glen Coppersmith,et al.  Exploratory Analysis of Social Media Prior to a Suicide Attempt , 2016, CLPsych@HLT-NAACL.

[20]  Mark Dredze,et al.  Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media , 2016, CHI.

[21]  Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, CLPsych@ACL 2014, Baltimore, Maryland, USA, June 27, 2004 , 2014, CLPsych@ACL.

[22]  Cindy K. Chung,et al.  The Psychological Functions of Function Words , 2007 .

[23]  Kevin A Padrez,et al.  Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department , 2015, BMJ Quality & Safety.

[24]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[25]  Thang Nguyen,et al.  The University of Maryland CLPsych 2015 Shared Task System , 2015, CLPsych@HLT-NAACL.

[26]  Gregory J. Park,et al.  Psychological Language on Twitter Predicts County-Level Heart Disease Mortality , 2015, Psychological science.

[27]  Joseph L. Annest,et al.  Suicide Among Adults Aged 35–64 Years — United States, 1999–2010 , 2013, MMWR. Morbidity and mortality weekly report.