Early Identification of Mental Health Disorder Employing Machine Learning-based Secure Edge Analytics

2021 ◽  
pp. 117-136
Author(s):  
Naveen Kumar Oburi ◽  
Tahrat Tazrin ◽  
Akshai Ramesh ◽  
Prem Sagar ◽  
Sadman Sakib ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Early Identification ◽  
Mental Health Disorder

scholarly journals Using Machine Learning To Understand Suicide: A New Approach To Classifying Australian Coroner’s Court Decisions

2021 ◽  
Author(s):  
Ravi Iyer ◽  
Elizabeth Seabrook ◽  
Suku Sukunesan ◽  
Maja Nedeljkovic ◽  
Denny Meyer
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Health Disorder ◽  
Supervised Machine Learning ◽  
Feature Engineering ◽  
Court Case ◽  
Learning Approach ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Case Files

Abstract We aimed to demonstrate how a large collection of publicly accessible Australian Coroner’s Court case files (n=4459) (2009-2019) can be automatically classified for determination of death by suicide, presence of mental health disorder and sex of deceased via Natural Language Processing (NLP) methods - supervised machine learning and unsupervised dictionary-based and string search based approaches. We achieved superior levels of accuracy in the machine learning classification (Gradient Boosting vs. Random Forest baseline) of deaths by suicide of 83.3% (sensitivity = 85.1%, Specificity = 79.1%) and an accuracy of 98.3% for the dictionary-based classification of mental health disorder, as defined by the OCD-10 (sensitivity = 99.0%, specificity = 97.9%). Our machine learning approach automatically classified 24.2% (1078/4459) of the case files as referring to deaths by suicide while 63.7% (2940/4459) where classified as exhibiting a mental health disorder1. We employed a two-stage machine learning approach involving feature engineering, followed by predictive modelling in the second. Feature engineering involved several steps including removal of low value text, parts of speech analysis, term document weighting and topic clustering. Predictive classification involved extensive hyperparameter tuning to yield the most accurate model. We validated our models against a manually pre-coded subsample of case files, and also via binary logistic regression to test the contribution of each classified mental health disorder against determinations of deaths by suicide according to extant literature. This validation step confirmed elevated odds of suicide attributed to diagnoses of Depression, Schizophrenia and Obsessive Compulsive Disorder. Finally, we offer a short case study to demonstrate the efficacy of our approach in investigating a subset of case findings referring to suicides resulting from family violence. We offer a proof of concept model that demonstrates an objective and scalable approach to the analysis of legal texts. The use of NLP methods in analysing Coroner's Court case findings has important implications for the ongoing development of a real-time surveillance of suicide system in Australia.

scholarly journals Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 444
Author(s):  
Isuri Anuradha Nanomi Arachchige ◽  
Priyadharshany Sandanapitchai ◽  
Ruvan Weerasinghe
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Mental Health Disorder ◽  
Online Support ◽  
Processing Techniques ◽  
Depression Disorder

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

Is Pacific language ability protective of prevalence of mental disorders among Pacific peoples in New Zealand?

2018 ◽  
Vol 21 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Eirenei Taua'i ◽  
Rose Richards ◽  
Jesse Kokaua
Keyword(s):  
Mental Health ◽  
New Zealand ◽  
Mental Health Services ◽  
Mental Disorders ◽  
Health Services ◽  
Common Mental Disorders ◽  
Mental Health Disorder ◽  
Positive Role ◽  
Pacific People ◽  
Language Promotion

Aims: To explore associations between experiences of mental illness, migration status and languages spoken among Pacific adults living in NZ. Methods: SURVEY FREQ and SURVEY LOGISTIC procedures in SAS were applied to data from Te Rau Hinengaro: The New Zealand (NZ) Mental Health Survey, a survey of 12,992 New Zealand adults aged 16 and over in 2003/2004. Pacific people were over sampled and this paper focuses on the 2374 Pacific participants but includes, for comparison, 8160 non-Maori-non-Pacific (NMNP) participants. Results: Pacific migrant respondents had the lowest prevalence of mental disorders compared to other Pacific peoples. However, Pacific immigrants were also less likely to use mental health services, suggesting an increased likelihood of experiencing barriers to available mental health care. Those who were born in NZ and who were proficient in a Pacific language had the lowest levels of common mental disorders, suggesting a protective effect for the NZ-born population. Additionally, access to mental health services was similar between NZ-born people who spoke a Pacific language and those who did not. Conclusions: We conclude that, given the association between Pacific language and reduced mental disorder, there may be a positive role for Pacific language promotion in efforts to reduce the prevalence of mental health disorder among Pacific communities in NZ.

Social support and mental health status of internal migrants in Beijing

2020 ◽  
Vol 48 (4) ◽  
pp. 1-16
Author(s):  
Ying Zhou ◽  
Jianhua Wang
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Health Status ◽  
Marital Status ◽  
Rating Scale ◽  
Mental Health Disorder ◽  
Mental Health Status ◽  
Self Report ◽  
Monthly Income ◽  
Internal Migrants

We investigated the mental health status of 320 internal migrants in Beijing according to gender, age, marital status, and monthly income, and examined the relationship between their mental health status and social support mechanisms. Participants completed the self-report Symptom Checklist-90-R and Social Support Rating Scale. Results showed that their mental health was significantly worse than the Chinese adult norm as assessed in 2017. Participants' social support varied according to age, marital status, and monthly income. Female participants younger than 30 years old with a monthly income lower than 3,000 yuan comprised the group with the most mental health disorder symptoms. They thus required greater personal attention to their health. The results suggested that social support can predict mental health among internal migrants. Directions for further research are discussed.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

scholarly journals Machine Learning in Mental Health

2020 ◽  
Vol 27 (5) ◽  
pp. 1-53 ◽  
Author(s):  
Anja Thieme ◽  
Danielle Belgrave ◽  
Gavin Doherty
Keyword(s):  
Mental Health ◽  
Machine Learning

scholarly journals Culture and psychopathology: An attempt at reconsidering the role of social learning

2021 ◽  
pp. 1-16
Author(s):  
Peter Fonagy ◽  
Chloe Campbell ◽  
Matthew Constantinou ◽  
Anna Higgitt ◽  
Elizabeth Allison ◽  
...  
Keyword(s):  
Mental Health ◽  
Affect Regulation ◽  
Developmental Psychopathology ◽  
Mental Health Disorder ◽  
Cultural Environment ◽  
Single Model ◽  
Developmental Approach ◽  
The Social ◽  
P Factor

Abstract This paper proposes a model for developmental psychopathology that is informed by recent research suggestive of a single model of mental health disorder (the p factor) and seeks to integrate the role of the wider social and cultural environment into our model, which has previously been more narrowly focused on the role of the immediate caregiving context. Informed by recently emerging thinking on the social and culturally driven nature of human cognitive development, the ways in which humans are primed to learn and communicate culture, and a mentalizing perspective on the highly intersubjective nature of our capacity for affect regulation and social functioning, we set out a cultural-developmental approach to psychopathology.

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