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

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.

Author(s):  
Frances Shaw

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.


2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield

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.


2020 ◽  
Author(s):  
Timea Mariann Helter ◽  
Joanna Coast ◽  
Agata Łaszewska ◽  
Tanja Stamm ◽  
Judit Simon

Abstract Background: There is increasing evidence that assessing outcomes in terms of capability wellbeing provides information beyond that of health-related quality of life measures for evaluation in mental health research. This paper aimed to contribute to the utilisation of capability-based measures relative to health status measures in mental health research, by exploring the empirical relationship between the OxCAP-MH, the ICECAP-A, and the EQ-5D instruments against condition-specific measures and each other for schizophrenic patients with depression. Methods: Using trial data for 100 patients from the UK, the properties of the instruments were compared in terms of convergent validity, including correlations between the OxCAP-MH, the ICECAP-A, the EQ-5D-5L descriptive system and the EQ-5D VAS scores; and a line of central tendency between two variables was plotted on locally weighted smoothing curves. Exploratory factor analysis (EFA) investigated the extent to which the instruments measured complementary or overlapping constructs. Responsiveness was assessed in terms of standardised response mean and correlation between change scores (baseline to endpoint) of the instruments. Results: Correlation between the OxCAP-MH and ICECAP-A baseline scores was strong (0.682) and between change scores was moderate (0.401). The baseline scores of both capability instruments correlated more with condition-specific (0.481-0.718) than with generic (0.344-0.425) instruments. Their change scores weakly correlated with change scores of the generic health-related scales (0.183-0.247), but moderately with those of condition-specific instruments (0.339-0.557). The EFA found that while the EQ-5D-5L descriptive system loaded onto one factor, the items of the ICECAP-A loaded onto two additional factors and the items of the OxCAP-MH loaded onto three additional factors. Conclusions: The capability instruments had stronger convergent validity with each other than with any of the other instruments. Assessing outcomes in terms of capabilities for schizophrenic patients with depression proved to capture broader relevant information than the EQ-5D-5L both by the OxCAP-MH and the ICECAP-A, albeit to different extent. When comparing the two capability instruments, the OxCAP-MH tended to have stronger correlations with condition-specific instruments most likely due to its origin from mental health outcome measurement, while the ICECAP-A had slightly stronger correlation with the EQ-5D VAS.


Author(s):  
Nancy Wolff

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.


2017 ◽  
Vol 34 (5) ◽  
pp. 196-195 ◽  
Author(s):  
Daniel Bone ◽  
Chi-Chun Lee ◽  
Theodora Chaspari ◽  
James Gibson ◽  
Shrikanth Narayanan

2014 ◽  
Vol 23 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Gillian Brown ◽  
Max Marshall ◽  
Peter Bower ◽  
Adrine Woodham ◽  
Waquas Waheed

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