scholarly journals DEVELOPMENT OF A CHATBOT TO IDENTIFY DEPRESSION THROUGH A QUESTIONNAIRE

2021 ◽  
Author(s):  
Stefano Neto Jai Hyun Choi ◽  
Rita Simone Lopes Moreira ◽  
Ana Luiza Fontes de Azevedo Costa ◽  
Caio Vinicius Saito Regatieri ◽  
Vagner Rogerio dos Santos
Keyword(s):  
Artificial Intelligence ◽  
Health Care Professionals ◽  
Depression Symptoms ◽  
Vision Disorders ◽  
Software Validation ◽  
Social Demography ◽  
The Social ◽  
Validation Tests ◽  
Real Patients ◽  
Satisfactory Manner

Purpose: to develop and test a prototype of Chatbot (Artificial Intelligence) with the purpose of applying a questionnaire to assess depression in visually impairmed invidivuals. Methods: This project was carried out in the Innovation in Health Technology Laboratory of the Sao Paulo Federal University. The Chatbot was developed using the platform BLiP. The social demography questionnaire and the Center for Epidemiological Scale Depression (CES D) were selected to collect the essential data and to identify the presence of depression, respectively. After the development, validation tests were applied to verify the functionality and structure of the chatbot. Results: The Chatbot prototype presented an excellent flow of conversation in the tests conducted. The questionnaires were applied in a satisfactory manner during the tests, showing that it could possibly be applied to real patients with depression symptoms. Software validation tests approved the prototypes function. Conclusions: The Chatbot prototype is an affordable and easy way to apply questionnaires that can be used to identify health conditions, such as the likelihood of being depressed. The Chatbot system can record the answers so it is analyzed by health care professionals to help decide if an intervention is necessary. KEYWORDS: Artificial Intelligence; Depression; Ophthalmology; Vision Disorders.

scholarly journals Association between the social support for mothers of patients with eating disorders, maternal mental health, and patient symptomatic severity: A cross-sectional study

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Atsurou Yamada ◽  
Fujika Katsuki ◽  
Masaki Kondo ◽  
Hanayo Sawada ◽  
Norio Watanabe ◽  
...  
Keyword(s):  
Social Support ◽  
Eating Disorders ◽  
Eating Disorder ◽  
Psychological Distress ◽  
Self Efficacy ◽  
Maternal Mental Health ◽  
Self Esteem ◽  
Depression Symptoms ◽  
The Social ◽  
Distress Scale

Abstract Background Although caregivers of patients with eating disorders usually experience a heavy caregiving burden, the effects of social support on caregivers of patients with eating disorders are unknown. This study aimed to investigate how social support for mothers who are caregivers of patients with an eating disorder improves the mothers’ mental status and, consequently, the symptoms and status of the patients. Methods Fifty-seven pairs of participants were recruited from four family self-help groups and one university hospital in Japan. Recruitment was conducted from July 2017 to August 2018. Mothers were evaluated for social support using the Japanese version of the Social Provisions Scale-10 item (SPS-10), self-efficacy using the General Self-Efficacy Scale, loneliness using the University of California, Los Angeles Loneliness Scale, listening attitude using the Active Listening Attitude Scale, family functioning using the Family Assessment Device, depression symptoms using the Beck Depression Inventory (Second Edition), and psychological distress using the Kessler Psychological Distress Scale. Patients were evaluated for self-esteem using the Rosenberg Self-Esteem Scale, assertion using the Youth Assertion Scale, and their symptoms using the Eating Disorder Inventory. We divided the mothers and patients into two groups based on the mean score of the SPS-10 of mothers and compared the status of mothers and patients between the high- and low-scoring groups. Results High social support for mothers of patients with eating disorders was significantly associated with lower scores for loneliness and depression of these mothers. We found no significant differences in any patient scores based on mothers’ level of social support. Conclusions For patients with eating disorders, social support for a caregiver cannot be expected to improve their symptoms, but it may help prevent caregiver depression and loneliness.

scholarly journals Group Agency and Artificial Intelligence

2021 ◽  
Author(s):  
Christian List
Keyword(s):  
Artificial Intelligence ◽  
Moral Status ◽  
Intelligent Systems ◽  
Group Agency ◽  
High Stakes ◽  
Moral Agents ◽  
Legal Personhood ◽  
The Social ◽  
Intentional Agents ◽  
Regulatory Challenges

AbstractThe aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical.

scholarly journals Where Do We Go After Surviving the Virus? Cross-Country Documentary Analysis of the Social Consequences Faced by COVID-19 Survivors

2021 ◽  
pp. 0272684X2110221
Author(s):  
Alfred F. S. Owusu ◽  
Alhassan Abdullah ◽  
Godfred H. Pinto ◽  
Hajara Bentum ◽  
Janet Tein Ni Moo ◽  
...  
Keyword(s):  
Health Care Professionals ◽  
Health Workers ◽  
Interprofessional Collaboration ◽  
Social Consequences ◽  
Medical Practitioners ◽  
News Reports ◽  
Documentary Analysis ◽  
The Social ◽  
Recovery Narratives ◽  
Personal Accounts

In this study, we attempted to move beyond the skewed discussions on stigma to unravel other social consequences that are experienced by persons who have recovered from COVID-19. We conducted a documentary review of published news reports from 14 highly ranked news portals in Ghana and Malaysia (published between 1st January 2020 and 30th August 2020) that contained personal accounts from the recovered patients about their lived experiences with the virus and social consequences encountered after recovery. Narratives from the recovered patients were extracted and analyzed following the narrative thematic analysis procedure. Common themes identified from the narratives included: 1) Stigma impacting mental health, 2) Assault and abuse 3) Experiences of treatment. The findings show the need for interprofessional collaboration between social and health care professionals such as social workers, community health workers, medical practitioners and psychologists to prevent and address issues of abuse and other social consequences experienced by COVID-19 survivors.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals The characteristics of anxiety and depression symptom severity in older adults living in public housing

2011 ◽  
Vol 24 (4) ◽  
pp. 614-623 ◽  
Author(s):  
Adam Simning ◽  
Yeates Conwell ◽  
Susan G. Fisher ◽  
Thomas M. Richardson ◽  
Edwin van Wijngaarden
Keyword(s):  
Public Housing ◽  
Older Adult ◽  
Symptom Severity ◽  
Regression Models ◽  
Late Life ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Depression Symptom ◽  
The Social ◽  
Public Housing Residents

ABSTRACTBackground:Anxiety and depression are common in older adult public housing residents and frequently co-occur. To understand anxiety and depression more fully in this socioeconomically disadvantaged population, this study relies on the Social Antecedent Model of Psychopathology to characterize anxiety and depression symptoms concurrently.Methods:190 public housing residents aged 60 years and older in Rochester, New York, participated in a research interview during which they reported on variables across the six stages of the Social Antecedent Model. GAD-7 and PHQ-9 assessed anxiety and depression symptoms, respectively.Results:In these older adult residents, anxiety and depression symptom severity scores were correlated (r = 0.61; p < 0.001). Correlates of anxiety and depression symptom severity were similar for both outcomes and spanned the six stages of the Social Antecedent Model. Multivariate linear regression models identified age, medical comorbidity, mobility, social support, maladaptive coping, and recent life events severity as statistically significant correlates. The regression models accounted for 43% of anxiety and 48% of depression symptom variability.Conclusions:In public housing residents, late-life anxiety and depression symptoms were moderately correlated. Anxiety symptom severity correlates were largely consistent with those found for depression symptom severity. The broad distribution of correlates across demographic, social, medical, and behavioral domains suggests that the context of late-life anxiety and depression symptomatology in public housing is complex and that multidisciplinary collaborative care approaches may be warranted in future interventions.

ARTIFICIAL INTELLIGENCE

2021 ◽  
pp. 56-63
Author(s):  
V. I. Matveev
Keyword(s):  
Artificial Intelligence ◽  
Social Environment ◽  
Science And Technology ◽  
Main Direction ◽  
Modern World ◽  
The Social ◽  
Legislative Measures

Artificial intelligence is becoming the main direction of the development of science and technology, making progress at a new level. Automation of production, the implementation of operations in hazardous and harmful areas, the implementation of routine actions in the environment are inevitable in the modern world. A person creates an analogue for himself, realizing the possible consequences and limiting them to legislative acts. The article provides positive examples of the implementation of the artificial intelligence project and legislative measures that limit its impact on the social environment.

scholarly journals Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)

2018 ◽  
Vol 10 (9) ◽  
pp. 3066 ◽  
Author(s):  
Vasile Gherheș ◽  
Ciprian Obrad
Keyword(s):  
Artificial Intelligence ◽  
Social Life ◽  
Research Method ◽  
Quantitative Research ◽  
Positive Attitude ◽  
Negative Impact ◽  
Representation Error ◽  
The Social ◽  
The Future ◽  
Humanities Students

This study investigates how the development of artificial intelligence (AI) is perceived by the students enrolled in technical and humanistic specializations at two universities in Timisoara. It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations associated with it, and on the possible impact of artificial intelligence on certain areas of the social life. Moreover, the present study reveals the students’ perceptions on the sustainability of these changes and developments, and therefore aims to reduce the possible negative impact on consumers, and at anticipate the changes that AI will produce in the future. In order to collect the data, the authors have used a quantitative research method. A questionnaire-based sociological survey was completed by 928 students, with a representation error of only ±3%. The analysis has shown that a great number of respondents have a positive attitude towards the emergence of AI, who believe it will influence society for the better. The results have also underscored underlying differences based on the respondents’ type of specialization (humanistic or technical), and their gender.

Caregivers' perspectives on the pre-diagnostic period in early onset dementia: a long and winding road

2011 ◽  
Vol 23 (9) ◽  
pp. 1393-1404 ◽  
Author(s):  
Deliane van Vliet ◽  
Marjolein E. de Vugt ◽  
Christian Bakker ◽  
Raymond T. C. M. Koopmans ◽  
Yolande A. L. Pijnenburg ◽  
...  
Keyword(s):  
Social Context ◽  
Health Care Professionals ◽  
Early Onset ◽  
Late Onset ◽  
Structured Interviews ◽  
Early Onset Dementia ◽  
The Social ◽  
The Family ◽  
Constant Comparative Analysis ◽  
Financial Issues

ABSTRACTBackground: Recognizing and diagnosing early onset dementia (EOD) can be complex and often takes longer than for late onset dementia. The objectives of this study are to investigate the barriers to diagnosis and to develop a typology of the diagnosis pathway for EOD caregivers.Methods: Semi-structured interviews with 92 EOD caregivers were analyzed using constant comparative analysis and grounded theory. A conceptual model was formed based on 21 interviews and tested in 29 additional transcripts. The identified categories were quantified in the whole sample.Results: Seven themes emerged: (1) changes in the family member, (2) disrupted family life, (3) misattribution, (4) denial and refusal to seek advice, (5) lack of confirmation from social context, (6) non-responsiveness of a general practitioner (GP), and (7) misdiagnosis. Cognitive and behavioral changes in the person with EOD were common and difficult to understand for caregivers. Marital difficulties, problems with children and work/financial issues were important topics. Confirmation of family members and being aware of problems at work were important for caregivers to notice deficits and/or seek help. Other main issues were a patient's refusal to seek help resulting from denial and inadequate help resulting from misdiagnosis.Conclusion: EOD caregivers experience a long and difficult period before diagnosis. We hypothesize that denial, refusal to seek help, misattribution of symptoms, lack of confirmation from the social context, professionals’ inadequate help and faulty diagnoses prolong the time before diagnosis. These findings underline the need for faster and more adequate help from health-care professionals and provide issues to focus on when supporting caregivers of people with EOD.

scholarly journals Relationship between social competences and the sense of general mental health and intensity of stress among nurses

2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Agnieszka Chrzan-Rodak ◽  
Barbara Ślusarska ◽  
Grzegorz Nowicki ◽  
Alina Deluga ◽  
Agnieszka Bartoszek
Keyword(s):  
Mental Health ◽  
Interpersonal Relationships ◽  
Perceived Stress ◽  
Positive Impact ◽  
General Health Questionnaire ◽  
Depression Symptoms ◽  
General Mental Health ◽  
The Social ◽  
The Mean ◽  
Social Competences

Introduction: Social competences are indispensable in occupations reliant on interpersonal interactions, such as in medical professions, e.g. nursing, conditioning not only the effective construction of interpersonal relationships, but ever more often emphasizing a positive impact on strengthening coping skills in stressful situations. The object of our study was to assess the connection of social competences with the sense of general mental health and intensity of stress in the group of nurses.Materials and methods: In the study took part 291 nurses (ages 23–63, mean job seniority 11 years, SD = 10.22). We used the Social Competence Questionnaire (KKS) according to Anna Matczak, the Perceived Stress Scale (PSS-10), as adapted by Zygfryd Juczyński and Nina Ogińska-Bulik, and the General Health Questionnaire (GHQ-28) in the adaptation of Zofia Makowska and Dorota Merecz to collect information for the study.Results: Stress intensity among respondents averaged 16.83 points (SD = 4.47). In the 4 analyzed indicators of the GHQ-28, the mean point score was: somatic symptoms M = 8.45, anxiety and insomnia M = 8.75, functional disorders M = 8.07, depression symptoms M = 2.46. 38.1% of the results of the level of general mental health were in the range 5–6, which is the average level of mental health perceived in the group of nurses.Conclusions: The level of perceived stress among more than half of the surveyed group of nurses was average (52.6%). The level of social competences is not significantly correlated with the intensity of stress experienced. The level of general mental health of 38.1% of the nurses fell in the range of average. The level of social competences significantly correlates with the general mental health status of the nurse respondents (R = -0.254, p < 0.001).

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