Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

Importance: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.

Objective: The study aimed to compare how different model metrics rank the importance of various covariates.

Design, Setting, And Participants: A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available.

Perimacular Atrophy Following Voretigene Neparvovec-Rzyl Treatment in the Setting of Previous Contralateral Eye Treatment With a Different Viral Vector.

Purpose: To report on cases of unilateral perimacular atrophy after treatment with voretigene neparvovec-rzyl, in the setting of previous contralateral eye treatment with a different viral vector.

Design: Single-center, retrospective chart review.

Methods: In this case series, four patients between the ages of six and 11 years old with RPE65-related retinopathy were treated unilaterally with rAAV2-CB-hRPE65 as part of a gene augmentation clinical trial (NCT00749957).

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.

Objective And Aims: Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity.

Methods: Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017-2020) were utilized with BMI 30 or higher defined as obesity.

Genomic loss of GPR108 disrupts AAV transduction in birds.

The G protein-coupled receptor 108 () gene encodes a protein factor identified as critical for adeno-associated virus (AAV) entry into mammalian cells, but whether it is universally involved in AAV transduction is unknown. Remarkably, we have discovered that is absent in the genomes of birds and in most other sauropsids, providing a likely explanation for the overall lower AAV transduction efficacy of common AAV serotypes in birds compared to mammals. Importantly, transgenic expression of human and manipulation of related glycan binding sites in the viral capsid significantly boost AAV transduction in zebra finch cells.

The contribution of low Apgar scores in identifying neonates with short-term morbidities in a large single center cohort.

Objective: To evaluate the association and utility of low 1- and 5-min Apgar scores to identify short-term morbidities in a large newborn cohort.

Methods: 15,542 infants >22 weeks gestation from a single center were included. Clinical data and low Apgar scores were analyzed for significance to ten short-term outcomes and were used to construct Receiver Operating Characteristic Curves and the AUC calculated for ten outcomes.

Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS) Utilizing the Nationwide Inpatient Sample 2020 in Individuals With Pancreatic Cysts and Lung Cancer.

This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis.

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020.

Importance: The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018.

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.

Importance: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population.

Background: Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions.

Objective: The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults.

Methods: A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020).

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information.

Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.

Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives.

Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations.

Background: Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern.

Objective: The study aimed to identify feature importance in a machine learning model using solely nutrition covariates.

Methods: A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020).

Increased vigorous exercise and decreased sedentary activities are associated with decreased depressive symptoms in United States adults: Analysis of The National Health and Nutrition Examination Survey (NHANES) 2017-2020.

Background And Aims: Depression is a major public health concern that affects over 4% of the global population. Identification of new nonpharmacologic recommendations will help decrease the burden of disease. The overarching of this study was to examine the association between physical activity and depressive symptoms in a large sample of adults in the United States.

Quantification of the Relationship of Pyridoxine and Spirometry Measurements in the United States Population.

Background: There has been evidence to suggest associations between vitamins and lung function.

Objective: This study aimed to examine the association between vitamin B6 and spirometry values.

Methods: A cross-sectional study was done using National Health and Nutritional Examination Surveys (NHANES) 2007-2012, which is a nationally representative, modern cohort.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database.

Background: There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

Objective: The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

Methods: Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%).

Diabetes is associated with increased risk of death in COVID-19 hospitalizations in Mexico 2020: A retrospective cohort study.

Background And Aim: The COVID-19 disease course can be thought of as a function of prior risk factors consisting of comorbidities and outcomes. Survival analysis data for diabetic patients with COVID-19 from an up to date and representative sample can increase efficiency in resource allocation. The study aimed to quantify mortality in Mexico for individuals with diabetes in the setting of COVID-19 hospitalization.

Quantification of the Effect of Vitamin E Intake on Depressive Symptoms in United States Adults Using Restricted Cubic Splines.

Background: Depression is a rapidly increasing public health concern, affecting >4% of the global population. Identification of new nutritional recommendations is needed to help combat this increasing public health concern.

Objectives: The study aimed to examine the association between vitamin E intake and depressive symptoms.

Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods.

Background And Aims: All fields have seen an increase in machine-learning techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to conduct a critical evaluation of the utilized model metrics, such as sensitivity, specificity, and area under the receiver operator characteristic curve (AUROC). For commonly used model metrics, we proposed the use of analytically derived distributions (ADDs) and compared it with simulation-based approaches.

Hospitalized COVID-19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death: A cross-sectional cohort study in Mexico in 2020.

Background: Diabetes mellitus is a chronic health condition that has been linked with an increased risk of severe illness and mortality from COVID-19. In Mexico, the impact of diabetes on COVID-19 outcomes in hospitalized patients has not been fully quantified. Understanding the increased risk posed by diabetes in this patient population can help healthcare providers better allocate resources and improve patient outcomes.

Use of machine learning to identify risk factors for coronary artery disease.

Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data.

Improved Rod Sensitivity as Assessed by Two-Color Dark-Adapted Perimetry in Patients With RPE65-Related Retinopathy Treated With Voretigene Neparvovec-rzyl.

Purpose: The purpose of this study was to evaluate rod-mediated function with two-color dark-adapted perimetry (2cDAP) in patients with RPE65-related retinopathy treated with voretigene neparvovec-rzyl.

Methods: Following dilation and dark adaptation, 2cDAP and FST were performed. The 2cDAP was measured on an Octopus 900 perimeter (Haag-Streit) with cyan (500 nm wavelength) and red (650 nm wavelength) stimuli.

Use of machine learning to identify risk factors for insomnia.

Importance: Sleep is critical to a person's physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders.

Objective: The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology.

Design, Setting, And Participants: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data.