Literature Collection

OBJECTIVES: Clinicians, researchers, and families consistently agree on the importance of transition clinics for adolescents with epilepsy. In order to ensure that all adolescents have a successful transition, it is important to consider and address the unique needs of each adolescent. The literature suggests that adolescents with varying cognitive abilities may have different needs when preparing for transition. In order to explore this further, this study aimed to better understand the unique psychosocial needs of adolescents with typical cognitive development, mild intellectual disability (MID), and of caregivers of adolescents with moderate-to-severe ID. METHODS: Baseline mental health, QoL, and transition readiness data from 231 transition-aged adolescents (aged 14 to 18) enrolled in an epilepsy transition clinic were analyzed for this study. For analyses, adolescents were separated by cognitive ability. RESULTS: Results showed that adolescents with differing cognitive abilities had different needs. Adolescents with typical cognitive development did not have all the skills needed for a successful transition, suggesting the need for more epilepsy education. Adolescents with MID had significantly fewer transition skills, suggesting the need for more accessible epilepsy information and education. Mental health comorbidities were significant considerations for both of these groups. When looking at caregivers of adolescents with moderate-to-severe cognitive impairment, results suggest that although they have necessary transition skills, they require supports in other areas. SIGNIFICANCE: In order to ensure best outcomes for these adolescents, it may be important to develop separate transition guidelines and programs for adolescents with differing cognitive abilities; helping to ensure that the needs of all adolescents are addressed.

BACKGROUND: Incorporation of cognitive screening into the busy primary care will require the development of highly efficient screening tools. We report the convergence validity of a very brief, self-administered, computerized assessment protocol against one of the most extensively used, clinician-administered instruments-the Montreal Cognitive Assessment (MoCA). METHOD: Two hundred six participants (mean age = 67.44, standard deviation [SD] = 11.63) completed the MoCA and the computerized test. Three machine learning algorithms (ie, Support Vector Machine, Random Forest, and Gradient Boosting Trees) were trained to classify participants according to the clinical cutoff score of the MoCA (ie, /=26, n = 165), suggesting greater sensitivity to age-related changes in cognitive functioning. CONCLUSION: Future studies should examine ways to improve the sensitivity of the computerized test by expanding the cognitive domains it measures without compromising its efficiency.

Cardiovascular disease continues to cause an important global health challenge, highlighting the critical importance of early detection in mitigating cardiac-related issues. There is a significant demand for reliable diagnostic alternatives. Taking advantage of health data through diverse machine learning algorithms may offer a more precise diagnostic approach. Machine learning-based decision support systems that utilize patients' clinical parameters present a promising solution for diagnosing cardiovascular disease. In this research, we collected extensive publicly available healthcare records. We integrated medical datasets based on common features to implement several machine learning models aimed at exploring the potential for more robust predictions of cardiovascular disease (CVD). The merged dataset initially contained 323,680 samples sourced from multiple databases. Following data preprocessing steps including cleaning, alignment of features, and removal of missing values, the final dataset consisted of 311,710 samples used for model training and evaluation. In our experiments, the CatBoost model achieved the highest area under the curve (AUC) of up to 94.1%.