US adults with unmet mental health treatment needs: Profiling and underlying causes using machine learning techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 186-199
Author(s):  
Sameer Kumar ◽  
Changyue Luo
Author(s):  
Tarun Jain ◽  
Ashish Jain ◽  
Priyank Singh Hada ◽  
Horesh Kumar ◽  
Vivek K Verma ◽  
...  

Author(s):  
Frances Shaw

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.


2020 ◽  
Vol 45 (6) ◽  
pp. 633-642
Author(s):  
Elizabeth R Wolock ◽  
Alexander H Queen ◽  
Gabriela M Rodríguez ◽  
John R Weisz

Abstract Objective In research with community samples, children with chronic physical illnesses have shown elevated anxiety and depressive symptoms, compared to healthy peers. Less is known about whether physical illnesses are associated with elevated internalizing symptoms even among children referred for mental health treatment—a pattern that would indicate distinctive treatment needs among physically ill children receiving mental health care. We investigated the relationship between chronic physical illness and internalizing symptomatology among children enrolling in outpatient mental health treatment. Method A total of 262 treatment-seeking children ages 7–15 and their caregivers completed a demographic questionnaire, Child Behavior Checklist, and Youth Self-Report during a pre-treatment assessment. Physical illnesses were identified through caregiver report. Results There was no overall association between the presence/absence of chronic physical illness and parent- or child-reported symptoms. However, number of chronic physical illnesses was related to parent- and child-reported affective symptoms. Children with two or more chronic physical illnesses had more severe depressive symptoms than those with fewer physical illnesses. Conclusion Having multiple chronic illnesses may elevate children’s risk of depression symptomatology, even in comparison to other children seeking mental health care. This suggests a need to identify factors that may exacerbate depression symptoms in physically ill children who are initiating therapy and to determine whether different or more intensive services may be helpful for this group. The findings suggest the potential utility of screening for depression in youth with chronic physical illnesses, as well as addressing mental and physical health concerns during treatment.


2021 ◽  
Author(s):  
Nisha Agnihotri

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>


2021 ◽  
Author(s):  
Nisha Agnihotri

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>


Author(s):  
Audrey L. Jones ◽  
Susan D. Cochran ◽  
Jane Rafferty ◽  
Robert Joseph Taylor ◽  
Vickie M. Mays

There is growing diversity within the Black population in the U.S., but limited understanding of ethnic and nativity differences in the mental health treatment needs of Black women. This study examined differences in the prevalence of psychiatric disorders, their persistence, and unmet treatment needs among Black women in the U.S. Data were from the National Survey of American Life, a nationally representative survey that assessed lifetime and twelve-month mood, anxiety, and substance use disorders according to the Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV) criteria, and mental health service use among those meeting disorder criteria. One in three African American women met criteria for a lifetime disorder, compared to one in three Caribbean women born within the U.S. and one in five Caribbean women born outside the U.S. About half of African American women with a lifetime disorder had a persistent psychiatric disorder, compared to two in five Caribbean women born within the U.S. and two in three Caribbean women born outside the U.S. African Americans had more persisting dysthymia and panic disorder and less persisting social phobia compared to foreign-born Caribbean women. Of the three groups, Caribbean women born within the U.S. were most likely to seek mental health treatment during their lifetime. These results demonstrate, despite a lower prevalence of psychiatric disorders in Black women, that there is a great likelihood their disorders will be marked by persistence and underscores the need for culturally specific treatment approaches. As Black immigrants in the United States are increasing in number, adequate mental health services are needed.


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