scholarly journals Authors' Reply to: What a Bibliometric Study Tells us about Social Media Mental Health Research, Comment on "Machine Learning for Mental Health in Social Media: Bibliometric Study". (Preprint)

2021 ◽  
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Health Research ◽  
Mental Health Research ◽  
Bibliometric Study

UNSTRUCTURED No abstract/not applicable

scholarly journals What a Bibliometric Study Tells us about Social Media Mental Health Research (Preprint)

2021 ◽  
Author(s):  
Philip Resnik ◽  
Munmun De Choudhury ◽  
Katherine Musacchio Schafer ◽  
Glen Coppersmith
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Research ◽  
Mental Health Research ◽  
Bibliometric Study

UNSTRUCTURED No abstract/not applicable

Machinic Empathy and Mental Health: The Relational Ethics of Machine Empathy and Artificial Intelligence in Her

2019 ◽  
pp. 158-175
Author(s):  
Frances Shaw
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Health Research ◽  
Mental Health Treatment ◽  
Mental Health Research ◽  
Relational Ethics ◽  
Health Treatment

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

Signal Processing and Machine Learning for Mental Health Research and Clinical Applications [Perspectives]

2017 ◽  
Vol 34 (5) ◽  
pp. 196-195 ◽  
Author(s):  
Daniel Bone ◽  
Chi-Chun Lee ◽  
Theodora Chaspari ◽  
James Gibson ◽  
Shrikanth Narayanan
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Signal Processing ◽  
Health Research ◽  
Mental Health Research ◽  
Clinical Applications

scholarly journals On the State of Social Media Data for Mental Health Research

2021 ◽  
Author(s):  
Keith Harrigian ◽  
Carlos Aguirre ◽  
Mark Dredze
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Research ◽  
Mental Health Research ◽  
The State ◽  
Social Media Data ◽  
Media Data

scholarly journals Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohammad Chowdhury ◽  
Eddie Gasca Cervantes ◽  
Wai-Yip Chan ◽  
Dallas P. Seitz
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Health Research ◽  
Mental Health Research ◽  
Risk Of Bias ◽  
Geriatric Mental Health ◽  
Electronic Health

Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.

scholarly journals Machine learning methods in psychiatry: a brief introduction

2020 ◽  
Vol 33 (1) ◽  
pp. e100171 ◽  
Author(s):  
Zhirou Zhou ◽  
Tsung-Chin Wu ◽  
Bokai Wang ◽  
Hongyue Wang ◽  
Xin M Tu ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Health Research ◽  
Mental Health Research ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health research.

scholarly journals Social media recruitment for mental health research: A systematic review

2020 ◽  
Vol 103 ◽  
pp. 152197
Author(s):  
Catherine Sanchez ◽  
Adrienne Grzenda ◽  
Andrea Varias ◽  
Alik S. Widge ◽  
Linda L. Carpenter ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Social Media ◽  
Health Research ◽  
Mental Health Research
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