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.

Correctional Mental Health Research and Program Evaluation

2015 ◽  
Author(s):  
Nancy Wolff
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Health Research ◽  
Mental Health Treatment ◽  
Mental Health Research ◽  
Psychosocial Treatment ◽  
Mental Illnesses ◽  
Health Treatment ◽  
Correctional Mental Health

Research in mental health issues in prisoner populations essentially stopped in the mid 1970’s. It is now re-emerging as a critical component of improving mental health care and helping toward recovery for the incarcerated mentally ill. Mental illness, ranging from acute anxiety to schizophrenia, is endemic within prisons and jails. Unlike their free world counterparts, however, incarcerated people have a constitutional right to mental health treatment. Yet, despite the need for and right to mental health treatment, remarkably little reliable and valid evidence is available on the nature and level of mental illness among incarcerated people, the effects of incarceration on symptomatology, the availability and quality of medication, cognitive, and psychosocial treatment for disorders, and how context impacts the effectiveness of the treatment that is available. Evidence is absent because corrections-based research is constrained by regulation, financing, and inexperience. In this chapter, the history of prisoner research and the evolution of federal regulations to protect prisoners as human subjects will be reviewed and then discussed in terms of how regulation has impacted correctional mental health research, after first defining what is meant by research and why research is needed to inform policy and practice decisions. This will be followed by recommendations for building the correctional mental health research evidence base. The intent here is to help researchers, in collaboration with stakeholders, develop, design, and implement research studies, and disseminate evidence to advance science and the quality of care available to incarcerated people with mental illnesses within the current regulatory environment.

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 Big data in mental health research – do the ns justify the means? Using large data-sets of electronic health records for mental health research

2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Data Storage ◽  
Health Research ◽  
Large Data ◽  
Population Data ◽  
Mental Health Research ◽  
Multiple Time ◽  
Short Account ◽  
Electronic Health

SummaryAdvances in information technology and data storage, so-called ‘big data’, have the potential to dramatically change the way we do research. We are presented with the possibility of whole-population data, collected over multiple time points and including detailed demographic information usually only available in expensive and labour-intensive surveys, but at a fraction of the cost and effort. Typically, accounts highlight the sheer volume of data available in terms of terabytes (1012) and petabytes (1015) of data while charting the exponential growth in computing power we can use to make sense of this. Presented with resources of such dizzying magnitude it is easy to lose sight of the potential limitations when the amount of data itself appears unlimited. In this short account I look at some recent advances in electronic health data that are relevant for mental health research while highlighting some of the potential pitfalls.

scholarly journals Big data: what it can and cannot achieve

2018 ◽  
Vol 24 (4) ◽  
pp. 237-244 ◽  
Author(s):  
Peter Schofield ◽  
Jayati Das-Munshi
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Health Research ◽  
Mental Health Research ◽  
Data Sources ◽  
List Type ◽  
Traditional Research ◽  
Research Designs ◽  
The Uk ◽  
Study Designs

SUMMARYThis article looks at the use of large datasets of health records, typically linked with other data sources, in mental health research. The most comprehensive examples of this kind of ‘big data’ are typically found in Scandinavian countries, although there are also many useful sources in the UK. There are a number of promising methodological innovations from studies using big data in UK mental health research, including: hybrid study designs, data linkage and enhanced study recruitment. It is, however, important to be aware of the limitations of research using big data, particularly the various pitfalls in analysis. We therefore caution against abandoning traditional research designs, and argue that other data sources are equally valuable and, ideally, research should incorporate data from a range of sources.LEARNING OBJECTIVES•Be aware of major big data resources relevant to mental health research•Be aware of key advantages and innovative study designs using these data sources•Understand the inherent limitations to studies reliant on big data aloneDECLARATION OF INTERESTNone.

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
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