Clarifying Relations Between ADHD and Functional Impairment in Adulthood: Utilization of Network and Machine Learning Approaches

Assessment ◽  
2021 ◽  
pp. 107319112110509
Author(s):  
Patrick K. Goh ◽  
Michelle M. Martel ◽  
Payton J. Jones ◽  
Pevitr S. Bansal ◽  
Ashley G. Eng ◽  
...  
Keyword(s):  
Random Forest ◽  
Leisure Activities ◽  
The United States ◽  
Learning Approaches ◽  
Adhd Symptoms ◽  
Random Forest Regression ◽  
Hyperactivity Disorder ◽  
Reduce Risk ◽  
Nationally Representative ◽  
Core Characteristics

Using network analysis and random forest regression, this study identified attention-deficit/hyperactivity disorder (ADHD) symptoms most important for indicating impairment in various functional domains. Participants comprised a nationally representative sample of 1249 adults in the United States. Bridge symptoms were identified as those demonstrating unique relations with impairment domains that, in total, were stronger than those involving other symptoms. Results suggested three inattentive (i.e., difficulty organizing; does not follow through; makes careless mistakes) and one hyperactive ( difficulty engaging in leisure activities) bridge symptoms. Random forest regression results supported bridge symptoms as most important (compared to other symptoms) for predicting global and specific impairment domains. Hyperactive/impulsive symptoms appeared more strongly related to impairment in women, whereas difficulty organizing and easily distracted appeared more related to impairment in men. Clarification of bridge symptoms may help identify core characteristics of ADHD in adulthood and specify screening and intervention targets to reduce risk for related impairment.

Introduction

CNS Spectrums ◽  
2007 ◽  
Vol 12 (S23) ◽  
pp. 4-5
Author(s):  
Lenard A. Adler ◽  
Jeffrey H. Newcorn
Keyword(s):  
Adult Adhd ◽  
The United States ◽  
Adhd Symptoms ◽  
Fourth Edition ◽  
Statistical Manual ◽  
Adhd Diagnosis ◽  
Text Revision ◽  
Hyperactivity Disorder ◽  
Diagnostic And Statistical Manual ◽  
Dsm Iv

Attention-deficit/hyperactivity disorder (ADHD) may be the most common chronic, undiagnosed psychiatric disorder in adults. ADHD is characterized by restlessness, overactivity, disorganization, impulsivity, and inattention; and as further characterized in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). For most cases, an adult ADHD diagnosis is preceded by symptoms in childhood, which is a time when the disorder is rarely inquired about and usually overlooked.ADHD has been recognized in children for several decades, and the importance of detection and treatment is well established. Whereas it was initially believed that children outgrew the disease, researchers now know that approximately two thirds of children affected with ADHD symptoms carry the condition into adolescence and then into adulthood. Consequently, >4% of adults in the United States have ADHD. Nevertheless, the disorder is unrecognized and untreated in the vast majority of these people.

scholarly journals Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 976
Author(s):  
Sunhae Kim ◽  
Hyekyung Lee ◽  
Kounseok Lee
Keyword(s):  
Machine Learning ◽  
Attention Deficit Hyperactivity Disorder ◽  
Random Forest ◽  
Attention Deficit ◽  
Adult Adhd ◽  
Minnesota Multiphasic Personality Inventory ◽  
Self Report ◽  
Adhd Symptoms ◽  
Linear Discriminant ◽  
Hyperactivity Disorder

(1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD.

scholarly journals A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Shrirang A. Kulkarni ◽  
Jodh S. Pannu ◽  
Andriy V. Koval ◽  
Gabriel J. Merrin ◽  
Varadraj P. Gurupur ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Physical Therapy ◽  
Random Forest ◽  
Generalized Additive Model ◽  
Additive Model ◽  
The United States ◽  
Random Forest Regression ◽  
Key Variables ◽  
Machine Learning Models

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.

Patterns of media use and leisure time among older adults

2021 ◽  
pp. 146144482110623
Author(s):  
Sonya Dal Cin ◽  
Matea Mustafaj ◽  
Karen Nielsen
Keyword(s):  
Older Adults ◽  
Leisure Time ◽  
Media Use ◽  
Leisure Activities ◽  
Well Being ◽  
The United States ◽  
Panel Survey ◽  
Patterns Of Use ◽  
Societal Participation ◽  
Nationally Representative

The aging population is rapidly growing both in the United States and many other parts of the world. Simultaneously, technology is rapidly progressing, and new forms of media have become integrated into daily life and societal participation. This study uses time diary data from a panel survey of members of nationally representative households ( N = 1776) to explore patterns of media use and functionally equivalent leisure time among older adults. The data support a three-profile typology of older adult use of media and non-media leisure activities. These include the computer socializer, the hobbyist, and the television watcher. We elaborate on these patterns of use and explore correlates with demographic and well-being variables. We find no evidence that well-being significantly differs across profiles of media use but identify income and employment status as potential drivers of older adults’ media activity, with implications for digital inequalities.

ADHD Symptoms, Entrepreneurial Orientation (EO), and Firm Performance

2019 ◽  
Vol 45 (1) ◽  
pp. 92-117 ◽  
Author(s):  
Wei Yu ◽  
Johan Wiklund ◽  
Ana Pérez-Luño
Keyword(s):  
United States ◽  
Firm Performance ◽  
Risk Taking ◽  
Entrepreneurial Orientation ◽  
Entrepreneurial Intention ◽  
The United States ◽  
Adhd Symptoms ◽  
Hyperactivity Disorder ◽  
Two Samples ◽  
The Relationship

Recently, scholars have started to investigate the relationship between ADHD (Attention Deficit Hyperactivity Disorder) symptoms and entrepreneurship, finding that ADHD symptoms positively impact entrepreneurial intention and action. However, the performance implications of ADHD symptoms are still unknown. Using two samples of entrepreneurs from the United States and Spain, we find evidence that impulsive and hyperactive symptoms of ADHD are largely conducive to firm performance through entrepreneurial orientation (EO) while inattention symptoms are not. This suggests that the performance advantages of entrepreneurs ADHD symptoms can be derived from greater focus on innovation, proactiveness, and risk-taking. We discuss the implications of our findings for the entrepreneurship literature.

The Association Between Physical Education and Symptoms of Attention Deficit Hyperactivity Disorder

2011 ◽  
Vol 8 (7) ◽  
pp. 964-970 ◽  
Author(s):  
Lucy Barnard-Brak ◽  
Tonya Davis ◽  
Tracey Sulak ◽  
Victor Brak
Keyword(s):  
Physical Activity ◽  
Attention Deficit Hyperactivity Disorder ◽  
Physical Education ◽  
Attention Deficit ◽  
Representative Sample ◽  
The United States ◽  
Equation Modeling ◽  
Community Based ◽  
Hyperactivity Disorder ◽  
Nationally Representative

Objective:The purpose of the current study was to examine the association between structured physical activity, specifically physical education, and symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Physical activity may be associated with lower levels of symptoms of ADHD and this rationale provided the impetus for the current study.Methods:A community-based, nationally representative sample of children from the Early Childhood Longitudinal Study, Kindergarten cohort (ECLS-K) was used. Structural equation modeling was used to examine the association of physical activity with symptoms of Attention Deficit Hyperactivity Disorder. Two random subsamples were drawn for the purposes of cross-validation of our model. Statistics reflecting model ft are reported.Results:With a standardized path coefficient value of –.23, findings from the current study indicate a significant, inverse association between physical education, as a structured form of physical activity, with the symptoms of Attention Deficit Hyperactivity Disorder in children.Conclusions:Using a community-based, nationally representative sample of children aged 5 to 7 years old from the United States, the results of the current study suggest that physical education, as a structured form of physical activity, may be considered as associated with lower levels of symptoms of ADHD across time.

Basic numerical processing, calculation, and working memory in children with dyscalculia and/or ADHD symptoms

2016 ◽  
Vol 44 (5) ◽  
pp. 365-375 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Elena Ise ◽  
Julia Raddatz ◽  
Christin Schwenk ◽  
Christian Dobel
Keyword(s):  
Working Memory ◽  
Processing Speed ◽  
Verbal Working Memory ◽  
Typically Developing ◽  
Cognitive Profiles ◽  
Adhd Symptoms ◽  
Memory Processing ◽  
Numerical Processing ◽  
Hyperactivity Disorder ◽  
Neurocognitive Measures

Abstract. Objective: Deficits in basic numerical skills, calculation, and working memory have been found in children with developmental dyscalculia (DD) as well as children with attention-deficit/hyperactivity disorder (ADHD). This paper investigates cognitive profiles of children with DD and/or ADHD symptoms (AS) in a double dissociation design to obtain a better understanding of the comorbidity of DD and ADHD. Method: Children with DD-only (N = 33), AS-only (N = 16), comorbid DD+AS (N = 20), and typically developing controls (TD, N = 40) were assessed on measures of basic numerical processing, calculation, working memory, processing speed, and neurocognitive measures of attention. Results: Children with DD (DD, DD+AS) showed deficits in all basic numerical skills, calculation, working memory, and sustained attention. Children with AS (AS, DD+AS) displayed more selective difficulties in dot enumeration, subtraction, verbal working memory, and processing speed. Also, they generally performed more poorly in neurocognitive measures of attention, especially alertness. Children with DD+AS mostly showed an additive combination of the deficits associated with DD-only and A_Sonly, except for subtraction tasks, in which they were less impaired than expected. Conclusions: DD and AS appear to be related to largely distinct patterns of cognitive deficits, which are present in combination in children with DD+AS.


Epilepsi dan Attention-Deficit / Hyperactivity Disorder (ADHD) pada Anak

2017 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Riki Sukiandra
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Brain Networks ◽  
Seizure Threshold ◽  
Childhood Epilepsy ◽  
Adhd Symptoms ◽  
Hyperactivity Disorder ◽  
Neurologic Disturbance ◽  
Inattentive Type ◽  
Child Age

Attention-deficit / hyperactivity disorder (ADHD) has been associated with childhood epilepsy. Epilepsy are themost common neurologic disturbance in child age. Children with epilepsy tend to get one or more ADHD symptoms,its related to lack of norepinephrine neurotransmitter in brain, that cause attenuate the effect of GABA and disruptionto fronto-striatal brain networks, these same brain networks are disrupted by seizures or the structural brainabnormalities that can cause seizures. Children with epilepsy especially absance, tend to get inattentive type ofADHD more than other types. Abnormalities of electro-encephalography found in inattentive type of ADHD withhigh focus activities in all lobe area. No data published that methylphenidate can lower seizure threshold or act asproconvulsant. Children with epilepsy tend to get one or more symptoms of ADHD in the following days.

The Racial and Economic Context of Trump Support: Evidence for Threat, Identity, and Contact Effects in the 2016 Presidential Election

2018 ◽  
Author(s):  
Eric Knowles ◽  
Linda Tropp
Keyword(s):  
The United States ◽  
Local Economy ◽  
Racial Identification ◽  
White Americans ◽  
Social Scientists ◽  
Neighborhood Diversity ◽  
Nationally Representative ◽  
To Come ◽  
Neighborhood Level ◽  
Support Evidence

Donald Trump's ascent to the Presidency of the United States defied the expectations of many social scientists, pundits, and laypeople. To date, most efforts to understand Trump's rise have focused on personality and demographic characteristics of White Americans. In contrast, the present work leverages a nationally representative sample of Whites to examine how contextual factors may have shaped support for Trump during the 2016 presidential primaries. Results reveal that neighborhood-level exposure to racial and ethnic minorities is associated with greater group threat and racial identification among Whites, as well as greater intentions to vote for Trump in the general election. At the same time, however, neighborhood diversity afforded Whites with opportunities for intergroup contact, which is associated with lower levels of threat, White identification, and Trump support. Further analyses suggest that a healthy local economy mutes threat effects in diverse contexts, allowing contact processes to come to the fore.

Sign in / Sign up

Export Citation Format

Share Document