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Full-Text Articles in Medicine and Health Sciences
Factors Affecting The Characterization Of Post-Exertional Malaise Derived From Patient Input, Carly S. Holtzman, Claire Fisher, Shaun Bhatia, Leonard A. Jason
Factors Affecting The Characterization Of Post-Exertional Malaise Derived From Patient Input, Carly S. Holtzman, Claire Fisher, Shaun Bhatia, Leonard A. Jason
Journal of Health Disparities Research and Practice
The National Institutes of Health/Center for Disease Control and Prevention (NIH/CDC) Common Data Elements (CDE) established a post-exertional malaise (PEM) workgroup with the task of describing PEM and recommending a standardized way of assessing it in patients with myalgic encephalomyelitis and chronic fatigue syndrome (ME/CFS). As a stigmatized group, patients with ME/CFS are in need of instruments which can properly describe their symptomatic experiences, which can help reduce the disparity between illness seriousness and appropriate attention from healthcare. The current study explored attitudes and preferences among 115 patients with ME/CFS who participated in the creation of a patient-driven instrument to …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …