Intelligence Mental Health Research Articles

The Role of Artificial Intelligence in Mental Health: A Review

The domains of mental health and artificial intelligence (AI) are undergoing rapid advancements, exhibiting the capacity to mutually influence one another in significant ways. The increasing prevalence of mental health illnesses has prompted the exploration of potential remedies in the field of AI, which show promise in the areas of early detection, prevention, and therapy. Sophisticated machine learning algorithms possess the capability to evaluate extensive volumes of data, including social media posts and voice patterns, with the objective of detecting patterns and symptoms associated with mental illness. This facilitates the implementation of more focused interventions and individualized treatment strategies. Furthermore, chatbots utilizing AI have the capability to deliver round-the-clock assistance to those undergoing acute distress or grant them access to therapy in cases where waiting lists are extensive. Nevertheless, it is of utmost importance to guarantee the incorporation of ethical issues throughout the use of AI in the field of mental healthcare. In order to achieve successful integration, it is imperative to address many concerns, including but not limited to privacy, bias, and accurate diagnosis. However, the convergence of mental health and AI offers a distinct prospect to transform our approach to mental disease and improve the availability of care for countless individuals globally.

Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia

Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia

Artificial and Human Intelligence in Mental Health

While technology has dramatically changed medical practice, various aspects of mental health practice and diagnosis remain almost unchanged across decades. Here we argue that artificial intelligence — with its capacity to learn and infer from data the workings of the human mind — may rapidly change this scenario. However, this process will not happen without friction and will promote an explicit reflection of the overarching goals and foundational aspects of mental health. We suggest that the converse relation is also very likely to happen. The application of artificial intelligence to a field that relates to the foundations of what makes us human — our volition, our thoughts, our pains and pleasures — may shift artificial intelligence back to its earliest days, when it was mostly conceived of as a laboratory to explore the limits and possibilities of human intelligence.

Artificial Intelligence in mental health and the biases of language based models.

The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within 'GloVe' and 'Word2Vec' word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.

Telepsychiatry and the Role of Artificial Intelligence in Mental Health in Post-COVID-19 India: A Scoping Review on Opportunities.

Background:COVID-19 has a profound impact on people with existing mental disorders, augmenting the prevailing inequalities in mental health.Methods:In order to understand the status of telepsychiatry in India and the role of artificial intelligence (AI) in mental health and its potential applications, a scoping review was done between March 2020 and May 2020. The literature review revealed 253 papers, which were used to derive the primary framework for analysis. The information was then reviewed for ideas and concepts, which were integrated with evidence from gray literature and categorized under broader themes based on the insights derived. Finally, a thematic framework was developed for discussion to tailor scientific information for decision-makers’ needs.Results:Review findings are summarized under the following headings: changing patterns of health-seeking behavior, origin and evolution of telepsychiatry, possible applications of telepsychiatry and AI, technological features, and AI models in mental health.Conclusions:Though there are several potential opportunities, the time is not yet ripe for telepsychiatry and AI to be adopted fully in the field of mental health care. But it is time that we develop indigenous proprietary technology and test and validate it. With many solutions offered by telepsychiatry and AI, psychiatrists must choose an appropriate tool based on their requirements, availability of resources, and feasibility of deployment. Harmony between conventional care and technology-based care must be reached gradually.

THE ROLE OF EMOTIONAL INTELLIGENCE IN MENTAL HEALTH OF TRIBAL ADOLESCENTS.

25May 2019 THE ROLE OF EMOTIONAL INTELLIGENCE IN MENTAL HEALTH OF TRIBAL ADOLESCENTS. Rajendra Kumar Meena. Post Doctoral fellow (UGC-NET, SET, Ph.D.), Department of Psychology, Mohanlal Sukhadia University, Udaipur.

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

Emotional Intelligence as Predictor of Mental Health among Chronic Disease Group

Health is considered a sense of wellbeing, inner happiness, and enthusiasm for living and harmony within self and with others, absence of disorders, conflicts, worries and anxieties. Emotional reactions and experiences affect both physical as well as psychological health. Negative emotional states are associated with unhealthy patterns of physiological functioning, whereas positive emotional states are associated with healthier patterns of responding in both cardiovascular activity and immune system. Taylor (2001) argues that emotionally intelligent people can cope better with life’s challenges and control their emotions more effectively, both of which contribute to good psychological and physical health. So the present study was designed to see the relationship between emotional intelligence and mental health and to see the role of emotional intelligence in mental health. Multidimensional Measure of Emotional Intelligence (MMEI by C. R. Darolia, 2003) and General Health Questionnaire (GHQ by Goldberg & Williams, 1988) was used. Sample of the study comprised of 200 chronic patients. Data obtained was analyzed by using co-relation and regression analysis. Results revealed emotional intelligence is positively correlated with mental health and emerged as predictor of mental health.

The Role of Adherence to Saying Prayers and Spiritual Intelligence in Mental Health of Tehrani High School Girls

The Role of Adherence to Saying Prayers and Spiritual Intelligence in Mental Health of Tehrani High School Girls

The role of Emotional Intelligence in mental health and Type D personality among young adults.

The concept of Emotional Intelligence (EI) was developed as a way to evaluate and highlight the importance of emotional health as it relates to overall quality of life. This study examines the predictive nature of EI with standardized measures of mental health to create a model that can be utilized to create more effective health promotion interventions. Step-wise multiple regression was used to predict mental health (Kessler K-6 scale) and Type D personality (Denollett's Scale of Negative Affectivity and Social Inhibition) with five dimensions of EI. The results revealed that while not all of the dimensions of EI regressed significantly in each model, mood management was highly predictive of all mental health measures under investigation. Cut-off points for each scale were also helpful in interpreting the relatedness of EI to mental health.

Emotional intelligence of mental health nurses

The aim of this study is to gain insight into the level of emotional intelligence of mental health nurses in the Netherlands. The focus in research on emotional intelligence to date has been on a variety of professionals. However, little is known about emotional intelligence in mental health nurses. The emotional intelligence of 98 Dutch nurses caring for psychiatric patients is reported. Data were collected with the Bar-On Emotional Quotient Inventory within a cross-sectional research design. The mean level of emotional intelligence of this sample of professionals is statistically significant higher than the emotional intelligence of the general population. Female nurses score significantly higher than men on the subscales Empathy, Social Responsibility, Interpersonal Relationship, Emotional Self-awareness, Self-Actualisation and Assertiveness. No correlations are found between years of experience and age on the one hand and emotional intelligence on the other hand. The results of this study show that nurses in psychiatric care indeed score above average in the emotional intelligence required to cope with the amount of emotional labour involved in daily mental health practice. The ascertained large range in emotional intelligence scores among the mental health nurses challenges us to investigate possible implications which higher or lower emotional intelligence levels may have on the quality of care. For instance, a possible relation between the level of emotional intelligence and the quality of the therapeutic nurse-patient relationship or the relation between the level of emotional intelligence and the manner of coping with situations characterised by a great amount of emotional labour (such as caring for patients who self-harm or are suicidal).

Emotional Intelligence

This article presents a framework for emotional intelligence, a set of skills hypothesized to contribute to the accurate appraisal and expression of emotion in oneself and in others, the effective regulation of emotion in self and others, and the use of feelings to motivate, plan, and achieve in one's life. We start by reviewing the debate about the adaptive versus maladaptive qualities of emotion. We then explore the literature on intelligence, and especially social intelligence, to examine the place of emotion in traditional intelligence conceptions. A framework for integrating the research on emotion-related skills is then described. Next, we review the components of emotional intelligence. To conclude the review, the role of emotional intelligence in mental health is discussed and avenues for further investigation are suggested.