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Articles 1 - 6 of 6
Full-Text Articles in Biomedical Informatics
Investigation Of The Utility Of A Ptsd Coaching Mobile App To Address Mental Health Services Demand In A Primary Care Clinic: Analysis Of Caps-5 Measures, Sindhura Nemani, Danielle Rae Schweitzer, Anne C. Jones
Investigation Of The Utility Of A Ptsd Coaching Mobile App To Address Mental Health Services Demand In A Primary Care Clinic: Analysis Of Caps-5 Measures, Sindhura Nemani, Danielle Rae Schweitzer, Anne C. Jones
Rowan-Virtua Research Day
The COVID-19 pandemic affected countless people globally, resulting in a greater need for mental health professionals and resources1. The demand for mental health care is soaring yet the limitation of resources has strained the healthcare system, making it challenging to help patients in a timely manner2. This poster represents a snapshot of a larger project that is striving to investigate an intervention to help bridge the gap between patient need and prompt referral through a warm handoff approach and use of the PTSD Coach mobile application as well as exploring patients’ satisfaction3,4,5,6,8,9. In this …
Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent
Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent
Rowan-Virtua Research Day
Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and …
Clinical Outcomes For Impella Patients Associated With Hyperlipidemia: An Analysis Of The National Inpatient Sample, Tony Elias, Sonika Vatsa, Kyrillos Girgis, Taha Syed, Rafail Beshai
Clinical Outcomes For Impella Patients Associated With Hyperlipidemia: An Analysis Of The National Inpatient Sample, Tony Elias, Sonika Vatsa, Kyrillos Girgis, Taha Syed, Rafail Beshai
Rowan-Virtua Research Day
The Impella, a ventricular assist device, is crucial for managing severe heart failure and cardiogenic shock. Despite its widespread use, there's scant information on how hyperlipidemia affects Impella patients. To address this gap, we delved into the National Inpatient Sample Database from 2019 and 2020. Our aim was to scrutinize in-hospital outcomes among these patients. We identified 8233 Impella patients, among whom 1012 (12.3%) had hyperlipidemia. Those with hyperlipidemia displayed higher rates of hypertension, diabetes mellitus, and chronic kidney disease compared to their counterparts without hyperlipidemia. Shockingly, in-hospital mortality was notably elevated in the hyperlipidemia group, emphasizing its clinical significance. …
Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen
Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen
College of Science & Mathematics Departmental Research
Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. …
Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle
Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle
Theses and Dissertations
Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of …
Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang
Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang
Rowan-Virtua Research Day
The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the …