A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection

2021 ◽  
Author(s):  
Piyush Kumar ◽  
Rishi Chauhan ◽  
Thompson Stephan ◽  
Achyut Shankar ◽  
Sanjeev Thakur
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Care ◽  
Smart Watch ◽  
Depression Detection ◽  
Health Care Application

scholarly journals Identifying Military Veterans in a Clinical Research Database using Natural Language Processing

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Daniel Leightley ◽  
Katharine M Mark ◽  
David Pernet ◽  
Dominic Murphy ◽  
Nicola T Fear ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
National Health Service ◽  
Mental Health Care ◽  
Health Care Services ◽  
Automatic Machine ◽  
Machine Learning Method ◽  
Learning Method ◽  
Care Services

BackgroundThere is a lack of quantitative evidence concerning United Kingdom veterans who access secondary mental health care. This is mainly due to a person’s veteran status not being routinely collected when they enter the health care system. Main AimThe study aimed to develop an NLP approach for identifying veterans accessing secondary mental health care services using National Health Service electronic health records. MethodsVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsThis study showed that it was possible to identify veterans using the NLP and machine learning approach on a sub-set of 4,200 patients. The automatic machine learning method was able to identify 270 veterans representing an accuracy of 97.2%. It is estimated to take between 6 to 16 minutes to manually search patient history statements whereas the automatic machine learning method took only one minute to run. ConclusionWe have shown that it is possible to identify veterans using NLP combined with machine learning. This work contributes towards the development of a more comprehensive picture of veterans who are accessing secondary mental health care services in the UK. It represents a first step in identifying veterans from one dataset and we hope that future research can inform the possibility of deploying the methods nationally. Despite our success in the current work, the tools are tailored to the SLaM dataset and future work is needed to develop a more agnostic framework. FundingForces in Mind Trust

scholarly journals Machine Learning in Clinical Psychology and Psychotherapy Education: A Survey of Postgraduate Students at a Swiss University

2020 ◽  
Author(s):  
Charlotte Blease ◽  
Anna Kharko ◽  
Marco Annoni ◽  
Jens Gaab ◽  
Cosima Locher
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Care ◽  
Clinical Psychology ◽  
Big Data Analytics ◽  
Mental Healthcare ◽  
Convenience Sample ◽  
Postgraduate Students ◽  
Psychotherapy Education

AbstractBackgroundThere is increasing use of for machine learning-enabled tools (e.g., psychotherapy apps) in mental health care.ObjectiveThis study aimed to explore postgraduate clinical psychology and psychotherapy students’ familiarity and formal exposure to topics related to artificial intelligence and machine learning (AI/ML) during their studies.MethodsIn April-June 2020, we conducted a mixed-methods web-based survey using a convenience sample of 120 clinical psychology and psychotherapy enrolled in a two-year Masters’ program students at a Swiss university.ResultsIn total 37 students responded (response rate: 37/120, 31%). Among the respondents, 73% (n=27) intended to enter a mental health profession. Among the students 97% reported that they had heard of the term ‘machine learning,’ and 78% reported that they were familiar with the concept of ‘big data analytics’. Students estimated 18.61/3600 hours, or 0.52% of their program would be spent on AI/ML education. Around half (46%) reported that they intended to learn about AI/ML as it pertained to mental health care. On 5-point Likert scale, students moderately agreed (median=4) that AI/M should be part of clinical psychology/psychotherapy education.ConclusionsEducation programs in clinical psychology/psychotherapy may lag developments in AI/ML-enabled tools in mental healthcare. This survey of postgraduate clinical psychology and psychotherapy students raises questions about how curricula could be enhanced to better prepare clinical psychology/psychotherapy trainees to engage in constructive debate about ethical and evidence-based issues pertaining to AI/ML tools, and in guiding patients on the use of online mental health services and apps.

scholarly journals An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19 (Preprint)

2020 ◽  
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Services ◽  
Health Services ◽  
Service Delivery ◽  
Mental Health Care ◽  
Real Time ◽  
Health Care Services ◽  
Care Services

UNSTRUCTURED In-person traditional approaches to mental health care services are facing difficulties amidst the coronavirus disease (COVID-19) crisis. The recent implementation of social distancing has redirected attention to nontraditional mental health care delivery to overcome hindrances to essential services. Telehealth has been established for several decades but has only been able to play a small role in health service delivery. Mobile and teledigital health solutions for mental health are well poised to respond to the upsurge in COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first-aid resources and targeting interventions. However, rigorous evaluation of these new opportunities is needed in terms of quality of interventions, effectiveness, and confidentiality. Service delivery could be broadened to include trained, unlicensed professionals, who may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members with stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking (with real-time automation and machine learning) of mental health in general populations as well as considerations and initiatives for underserved and vulnerable subpopulations.

scholarly journals Report on People's Mental Health Survey During Covid19

2020 ◽  
Author(s):  
Tam ngoc Nguyen
Keyword(s):  
Mental Health ◽  
High School ◽  
Health Care ◽  
Mental Health Care ◽  
Health Survey ◽  
Mental Health Survey ◽  
Health Care Application

This report contains latest responses on people's mental health during Covid19 pandemic. It also highlights the need for accessible mental health care application. Invitations (https://lnkd.in/e3Ua_DD) were sent out to US citizens with at least high school degrees. We use Qualtrics system and its advanced features of anti survey stuffing, fraud scoring, and so on.

scholarly journals An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19

10.2196/21718 ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. e21718 ◽  
Author(s):  
Luke Balcombe ◽  
Diego De Leo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Services ◽  
Health Services ◽  
Service Delivery ◽  
Mental Health Care ◽  
Real Time ◽  
Health Care Services ◽  
Care Services

In-person traditional approaches to mental health care services are facing difficulties amidst the coronavirus disease (COVID-19) crisis. The recent implementation of social distancing has redirected attention to nontraditional mental health care delivery to overcome hindrances to essential services. Telehealth has been established for several decades but has only been able to play a small role in health service delivery. Mobile and teledigital health solutions for mental health are well poised to respond to the upsurge in COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first-aid resources and targeting interventions. However, rigorous evaluation of these new opportunities is needed in terms of quality of interventions, effectiveness, and confidentiality. Service delivery could be broadened to include trained, unlicensed professionals, who may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members with stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking (with real-time automation and machine learning) of mental health in general populations as well as considerations and initiatives for underserved and vulnerable subpopulations.

A Tribute to the Late William J. Curran

1996 ◽  
Vol 24 (3) ◽  
pp. 274-275
Author(s):  
O. Lawrence ◽  
J.D. Gostin
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Illness ◽  
United Nations ◽  
Mental Health Care ◽  
Harvard University ◽  
Legal Medicine ◽  
General Assembly ◽  
International Institute ◽  
University Professor

In the summer of 1979, a group of experts on law, medicine, and ethics assembled in Siracusa, Sicily, under the auspices of the International Commission of Jurists and the International Institute of Higher Studies in Criminal Science, to draft guidelines on the rights of persons with mental illness. Sitting across the table from me was a quiet, proud man of distinctive intelligence, William J. Curran, Frances Glessner Lee Professor of Legal Medicine at Harvard University. Professor Curran was one of the principal drafters of those guidelines. Many years later in 1991, after several subsequent re-drafts by United Nations (U.N.) Rapporteur Erica-Irene Daes, the text was adopted by the U.N. General Assembly as the Principles for the Protection of Persons with Mental Illness and for the Improvement of Mental Health Care. This was the kind of remarkable achievement in the field of law and medicine that Professor Curran repeated throughout his distinguished career.

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