scholarly journals An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 149
Author(s):  
Charis Ntakolia ◽  
Dimitrios Priftis ◽  
Mariana Charakopoulou-Travlou ◽  
Ioanna Rannou ◽  
Konstantina Magklara ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
Mood State ◽  
Selection Process ◽  
World Health ◽  
Children And Adolescent ◽  
Health Organization ◽  
Almost All

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.

scholarly journals An Explainable Machine Learning Approach for COVID-19 Impact on Mood State of Children and Adolescents during the 1st Lockdown in Greece

2021 ◽  
Author(s):  
Charis Ntakolia ◽  
Dimitrios Priftis ◽  
Mariana Charakopoulou-Travlou ◽  
Ioanna Rannou ◽  
Konstantina Magklara ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
Mood State ◽  
Selection Process ◽  
World Health ◽  
Children And Adolescent ◽  
Health Organization ◽  
Almost All

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Psychological/psychiatric impact of the novel coronavirus outbreak: lessons learnt from China and call for timely crisis interventions in Italy

2020 ◽  
Author(s):  
Patrizia Zeppegno ◽  
Carla Gramaglia ◽  
Chiara Guerriero ◽  
Fabio Madeddu ◽  
Raffaella Calati
Keyword(s):  
Mental Health ◽  
Migrant Workers ◽  
Mental Health Problems ◽  
Virus Disease ◽  
Selection Process ◽  
Psychiatric Patients ◽  
Homeless People ◽  
World Health ◽  
Rapid Review ◽  
Post Traumatic Stress

Background: The World Health Organization declared the Corona Virus Disease 19 (Covid-19) a pandemic in March 2020. Psychological impact of Covid-19 can be consisent and should be prevented with adequate measures. Methods: We performed a literature mini review searching for studies in PubMed focusing on the psychological/psychiatric impact of Covid-19.Results: The selection process yielded 34 papers focusing on the relation between Covid-19 and mental health: 9 correspondence, 8 letters to the editor, 7 commentaries, 3 editorials, 4 original studies, 2 brief reports, and 1 a rapid review. The majority of the articles were performed in China. They focused on the general population and particular categories considered more fragile, e.g., psychiatric patients, older adults, international migrant workers, homeless people. Authors are unanimous in believing that Covid-19 will likely increase the risk of mental health problems and worsen existing psychiatric disorders/symptoms in patients, exposed subjects, and staff. Together with the negative emotionality related to the unpredictability of the situation, uncertainty concerning the risk, excessive fear, fear of death, loneliness, guilt, stigma, denial, anger, frustration, boredome, some symptoms might appear such as insomnia until patophobia (specifically, coronaphobia), depressive and anxiety disorders, post-traumatic stress disorder, and suicidal risk.Limitations: Literature is rapidly increasing and present results are only partial. Conclusions: Mental health care should not be overlooked in this moment. The experience of China should be of help for all the countries facing with Covid-19, among them Italy.

scholarly journals COVID-19 pandemic: A review of the global lockdown and its far-reaching effects

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110198
Author(s):  
Helen Onyeaka ◽  
Christian K Anumudu ◽  
Zainab T Al-Sharify ◽  
Esther Egele-Godswill ◽  
Paul Mbaegbu
Keyword(s):  
Mental Health ◽  
Global Economy ◽  
World Health ◽  
Global Pandemic ◽  
The World ◽  
Education Delivery ◽  
Environmental Air ◽  
Health Organization ◽  
Almost All ◽  
The Impact

COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was declared a pandemic by the World Health Organization (WHO) on the 11th of March 2020, leading to some form of lockdown across almost all countries of the world. The extent of the global pandemic due to COVID-19 has a significant impact on our lives that must be studied carefully to combat it. This study highlights the impacts of the COVID-19 pandemic lockdown on crucial aspects of daily life globally, including; Food security, Global economy, Education, Tourism, hospitality, sports and leisure, Gender Relation, Domestic Violence/Abuse, Mental Health and Environmental air pollution through a systematic search of the literature. The COVID-19 global lockdown was initiated to stem the spread of the virus and ‘flatten the curve’ of the pandemic. However, the impact of the lockdown has had far-reaching effects in different strata of life, including; changes in the accessibility and structure of education delivery to students, food insecurity as a result of unavailability and fluctuation in prices, the depression of the global economy, increase in mental health challenges, wellbeing and quality of life amongst others. This review article highlights the impacts of the COVID-19 pandemic lockdown across the globe. As the global lockdown is being lifted in a phased manner in various countries of the world, it is necessary to explore its impacts to understand its consequences comprehensively. This will guide future decisions that will be made in a possible future wave of the COVID-19 pandemic or other global disease outbreak.

The scale of the problem

Psychiatry ◽  
2019 ◽  
Author(s):  
Rebecca McKnight ◽  
Jonathan Price ◽  
John Geddes
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Mental Health Problems ◽  
Clinical Medicine ◽  
Well Being ◽  
World Health ◽  
The Social ◽  
Definition Of ◽  
Health Organization

One in four individuals suffer from a psychiatric disorder at some point in their life, with 15– 20 per cent fitting cri­teria for a mental disorder at any given time. The latter corresponds to around 450 million people worldwide, placing mental disorders as one of the leading causes of global morbidity. Mental health problems represent five of the ten leading causes of disability worldwide. The World Health Organization (WHO) reported in mid 2016 that ‘the global cost of mental illness is £651 billion per year’, stating that the equivalent of 50 million working years was being lost annually due to mental disorders. The financial global impact is clearly vast, but on a smaller scale, the social and psychological impacts of having a mental dis­order on yourself or your family are greater still. It is often difficult for the general public and clin­icians outside psychiatry to think of mental health dis­orders as ‘diseases’ because it is harder to pinpoint a specific pathological cause for them. When confronted with this view, it is helpful to consider that most of medicine was actually founded on this basis. For ex­ample, although medicine has been a profession for the past 2500 years, it was only in the late 1980s that Helicobacter pylori was linked to gastric/ duodenal ul­cers and gastric carcinoma, or more recently still that the BRCA genes were found to be a cause of breast cancer. Still much of clinical medicine treats a patient’s symptoms rather than objective abnormalities. The WHO has given the following definition of mental health:… Mental health is defined as a state of well- being in which every individual realizes his or her own po­tential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community.… This is a helpful definition, because it clearly defines a mental disorder as a condition that disrupts this state in any way, and sets clear goals of treatment for the clinician. It identifies the fact that a disruption of an individual’s mental health impacts negatively not only upon their enjoyment and ability to cope with life, but also upon that of the wider community.

Psychiatric morbidity in December 2015 flood-affected population in Tamil Nadu, India

2019 ◽  
Vol 65 (4) ◽  
pp. 338-344 ◽  
Author(s):  
Shailaja Bandla ◽  
NR Nappinnai ◽  
Srinivasagopalan Gopalasamy
Keyword(s):  
Mental Health ◽  
Tamil Nadu ◽  
Mental Health Problems ◽  
Negative Impact ◽  
Psychiatric Morbidity ◽  
Well Being ◽  
World Health ◽  
Post Traumatic Stress ◽  
Chi Square ◽  
Health Organization

Background: Floods are the most common type of natural disaster, which have a negative impact on mental health. Following floods, survivors are vulnerable to develop PTSD (post-traumatic stress disorder), depression, anxiety and other mental health problems. Aim: The aim is to study the psychiatric morbidity in the persons affected by floods during December 2015. Materials and methods: This study was carried out in Chennai and Cuddalore. In total, 223 persons who were directly exposed to floods were assessed. PTSD Checklist–Civilian Version, Beck’s Depression Inventory, Beck’s Anxiety Inventory and World Health Organization–Five Well-Being Scale (WHO-5) were used in the study. Chi-square test was used to compare the means. Results: Overall, psychiatric morbidity was found to be 45.29%; 60 (26.9%) persons had symptoms of PTSD. Anxiety was found in 48 (27.4%) and depression was found in 101 (45.29%) persons; and 11 (4.9%) persons have reported an increase in substance abuse. Conclusion: Following disaster like floods, there is a need for better preparedness in terms of basic necessities and medical and psychological assistance, particularly emphasizing the needs of older persons in order to prevent the development of psychiatric problems.

scholarly journals How do we engage global communities in the de-stigmatisation of mental illness?

2021 ◽  
Vol 30 (3) ◽  
pp. 184-187
Author(s):  
Paul Illingworth
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Mental Health Problems ◽  
Good Health ◽  
World Health ◽  
Middle Income ◽  
Low Middle Income Countries ◽  
Stigma Of Mental Illness ◽  
Development Goals ◽  
Health Organization

The World Health Organization (WHO) has acknowledged that high-income countries often address discrimination against people with mental health problems, but that low/middle income countries often have significant gaps in their approach to this subject—in how they measure the problem, and in strategies, policies and programmes to prevent it. Localised actions have occurred. These include the Hong Kong government's 2017 international conference on overcoming the stigma of mental illness, and the 2018 London Global Ministerial Mental Health Summit. Furthermore, the UK's Medical Research Council has funded Professor Graham Thornicroft (an expert in mental health discrimination and stigma) to undertake a global study. These and other approaches are welcome and bring improvements; however, they often rely on traditional westernised, ‘global north’ views/approaches. Given the rapid global demographic changes/dynamics and the lack of evidence demonstrating progress towards positive mental health globally, it is time to consider alternative and transformative approaches that encompasses diverse cultures and societies and aligns to the United Nations' Sustainable Development Goals (SDGs), specifically UN SDG 3 (Good health and wellbeing). This article describes the need for the change and suggests how positive change can be achieved through transnational inclusive mental health de-stigmatising education.

scholarly journals Detection of child depression using machine learning methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261131
Author(s):  
Umme Marzia Haque ◽  
Enamul Kabir ◽  
Rasheda Khanam
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Illness ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
Confusion Matrix ◽  
Model Performance ◽  
Child And Adolescent ◽  
Target Variable ◽  
Depression Detection

Background Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate signs to anticipate mental illness as depression in children and adolescents is vital in making an early and accurate diagnosis to avoid severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4–17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). As a result, our objective is to 1) create a model that can predict depression in children and adolescents aged 4–17 years old, 2) evaluate the results of ML algorithms to determine which one outperforms the others and 3) associate with the related issues of family activities and socioeconomic difficulties that contribute to depression. Methods The YMM, the second Australian Child and Adolescent Survey of Mental Health and Wellbeing 2013–14 has been used as data source in this research. The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. The Tree-based Pipeline Optimization Tool (TPOTclassifier) has been used to choose suitable supervised learning models. In the depression detection step, RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been used. Results Unhappy, nothing fun, irritable mood, diminished interest, weight loss/gain, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue, thinking or concentration problems or indecisiveness, suicide attempt or plan, presence of any of these five symptoms have been identified as 11 important features to detect depression among children and adolescents. Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms). Conclusion This RF-based prediction model is more accurate and informative in predicting child and adolescent depression that outperforms in all four confusion matrix performance measures as well as execution duration.

scholarly journals Innovative Ways to Promote Children’s Mental Health: Developing Accessible and Sustainable School Mental Health Services in Pakistan

2018 ◽  
Vol 24 (1) ◽  
pp. 565-567
Author(s):  
Nazish Imran
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Health Services ◽  
Mental Health Problems ◽  
School Mental Health ◽  
World Health ◽  
Childhood And Adolescence ◽  
Young Population ◽  
The Third ◽  
Health Organization

According to World Health Organization (WHO), approximately 10-15% of children and adolescents worldwide suffer from mental health problems.(1) The WHO also highlights that “Lack of attention to mental health of children & adolescents may lead to mental disorders with lifelong consequences, undermines compliance with health regimens and reduces the capacity of societies to be safe and productive”. (2) More than half of all mental disorders have an onset in childhood and adolescence with suicide being the third leading cause of death among adolescents. (1), (3) Child & adolescent mental health thus needs to be considered & emphasized as an integral component of overall health & growth of young population. Youth with positive mental health have positive self-efficacy beliefs, are productive and able to tackle developmental challenges adequately.

Global Mental Health Issues

2019 ◽  
pp. 53-70
Author(s):  
Judith K. Bass ◽  
Emily E. Haroz ◽  
Norman Sartorius
Keyword(s):  
Mental Health ◽  
Mental Health Problems ◽  
Global Mental Health ◽  
Social Situation ◽  
World Health ◽  
Contextual Influences ◽  
Middle Income ◽  
Middle Income Countries ◽  
Treatment And Prevention ◽  
Health Organization

This chapter reviews cultural and contextual influences on the presentation and prevalence of mental health problems in low- and middle-income countries. “Culture” is defined as shared norms, beliefs, values, and attitudes, while “context” refers to resource availability and political/social situation. The chapter includes discussion of the local “emic” and universal “etic” approaches to understanding mental health and shows the ways in which cultural and contextual variation influences the understanding, presentation, and treatment of mental and behavioral disorders. Research on understanding the effects of culture on differences in diagnosis and prevalence of mental disorders, as well as processes of recovery, is reviewed as well as guidelines developed by the World Health Organization. The way in which cultural and contextual differences affect choice and implementation of treatment and prevention programs is discussed.

scholarly journals Systematic Literature Review: Implications of Mental Illness on Life and Working Integrity

2021 ◽  
Vol 12 (2) ◽  
pp. 677-685
Author(s):  
Nor Shela Saleh, Et. al.
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Health Knowledge ◽  
Mental Health Problems ◽  
The Elderly ◽  
Health Problems ◽  
World Health ◽  
Negative Effects ◽  
Brain Disorder ◽  
Health Organization

Mental health problems in society are becoming new and more distressing as explained by the Ministry of Health Malaysia and the World Health Organization. Mental problems occur amongst working adults, the elderly, adolescents and children, men and women. Recent research confirms that the causes of mental health problems are due to genetic problems, personal problems, financial problems, learning pressures and stress at workplace. The effects of mental health problems can lead to depression, emotional tension, personality disorder and suicide. In order to obtain a wealth of information, researchers have accompanied critical analysis studies by viewing at several empirically established from previous studies. The first finding shows that mental illness classified as a brain disorder, an emotional disorder and an abnormal attitude. The second finding explains that the level of mental health knowledge among students is still low. The third study examines the causes of schizophrenia, which have four causes of the disease, namely genetic factors, drugs, work stress and poverty. The fourth finding explains the experience of caring for a mentally ill person who is also having trouble in the life of the patient's family. Generally, this study explains that mental illness is a problem that has various negative effects on various people. Implications of mental illness transpire over the long term if treatment not properly performed. Therefore, proactive initiatives prerequisite to be taken by all parties to ensure the quality of life of the people will perfect and normal devoid of interruption of mental illness.

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