Graph-Based Regularization for Transductive Class-Membership Prediction

2014 ◽  
pp. 202-218
Author(s):  
Pasquale Minervini ◽  
Claudia d’Amato ◽  
Nicola Fanizzi ◽  
Floriana Esposito
Keyword(s):  
Class Membership

Health Care Utilization, Transition Readiness, and Quality of Life: A Latent Class Analysis

2020 ◽  
Author(s):  
Katherine A Traino ◽  
Christina M Sharkey ◽  
Megan N Perez ◽  
Dana M Bakula ◽  
Caroline M Roberts ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Health Care Utilization ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Care Utilization ◽  
Class Membership ◽  
Transition Readiness ◽  
High Utilization

Abstract Objective To identify possible subgroups of health care utilization (HCU) patterns among adolescents and young adults (AYAs) with a chronic medical condition (CMC), and examine how these patterns relate to transition readiness and health-related quality of life (HRQoL). Methods Undergraduates (N = 359; Mage=19.51 years, SD = 1.31) with a self-reported CMC (e.g., asthma, allergies, irritable bowel syndrome) completed measures of demographics, HCU (e.g., presence of specialty or adult providers, recent medical visits), transition readiness, and mental HRQoL (MHC) and physical HRQoL (PHC). Latent class analysis identified four distinct patterns of HCU. The BCH procedure evaluated how these patterns related to transition readiness and HRQoL outcomes. Results Based on seven indicators of HCU, a four-class model was found to have optimal fit. Classes were termed High Utilization (n = 95), Adult Primary Care Physician (PCP)-Moderate Utilization (n = 107), Family PCP-Moderate Utilization (n = 81), and Low Utilization (n = 76). Age, family income, and illness controllability predicted class membership. Class membership predicted transition readiness and PHC, but not MHC. The High Utilization group reported the highest transition readiness and the lowest HRQoL, while the Low Utilization group reported the lowest transition readiness and highest HRQoL. Conclusions The present study characterizes the varying degrees to which AYAs with CMCs utilize health care. Our findings suggest poorer PHC may result in higher HCU, and that greater skills and health care engagement may not be sufficient for optimizing HRQoL. Future research should examine the High Utilization subgroup and their risk for poorer HRQoL.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

scholarly journals Prediction of Solubility and Permeability Class Membership: Provisional BCS Classification of the World’s Top Oral Drugs

2009 ◽  
Vol 11 (4) ◽  
pp. 740-746 ◽  
Author(s):  
Arik Dahan ◽  
Jonathan M. Miller ◽  
Gordon L. Amidon
Keyword(s):  
Class Membership ◽  
Oral Drugs

scholarly journals Trajectory classes of cannabis use and heavy drinking among rural African American adolescents: multi-level predictors of class membership

Addiction ◽  
2018 ◽  
Vol 113 (8) ◽  
pp. 1439-1449 ◽  
Author(s):  
Allen W. Barton ◽  
Gene H. Brody ◽  
Tamika C. B. Zapolski ◽  
Trenette C. Goings ◽  
Steven M. Kogan ◽  
...  
Keyword(s):  
African American ◽  
Heavy Drinking ◽  
African American Adolescents ◽  
Cannabis Use ◽  
Class Membership ◽  
Multi Level

scholarly journals Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

2018 ◽  
Vol 32 (3) ◽  
pp. 362-385 ◽  
Author(s):  
Shrikant A. Mehre ◽  
Ashis Kumar Dhara ◽  
Mandeep Garg ◽  
Naveen Kalra ◽  
Niranjan Khandelwal ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Pulmonary Nodules ◽  
Content Based Image Retrieval ◽  
Class Membership ◽  
Feature Sets ◽  
Image Retrieval System ◽  
Optimal Feature

scholarly journals Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Adult Population ◽  
Class Analysis ◽  
Class Membership ◽  
Anthropometric Measures ◽  
Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

scholarly journals Estimation of Class Membership Probabilities by Using Multiple Classification Scores

2008 ◽  
Vol 15 (2) ◽  
pp. 3-38 ◽  
Author(s):  
KAZUKO TAKAHASHI ◽  
HIROYA TAKAMURA ◽  
MANABU OKUMURA
Keyword(s):  
Class Membership ◽  
Multiple Classification
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