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Articles 1 - 4 of 4
Full-Text Articles in Medicine and Health Sciences
Time-To-Event Modeling For Hospital Length Of Stay Prediction For Covid-19 Patients, Yuxin Wen, Md. Fashiar Rahman, Yan Zhuang, Michael Pokojovy, Honglun Xu, Peter Mccaffrey, Alexander Vo, Eric Walser, Scott Moen, Tzu-Liang Bill Tseng
Time-To-Event Modeling For Hospital Length Of Stay Prediction For Covid-19 Patients, Yuxin Wen, Md. Fashiar Rahman, Yan Zhuang, Michael Pokojovy, Honglun Xu, Peter Mccaffrey, Alexander Vo, Eric Walser, Scott Moen, Tzu-Liang Bill Tseng
Engineering Faculty Articles and Research
Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six …
When Worlds Collide: Boundary Management Of Adolescent And Young Adult Childhood Cancer Survivors And Caregivers, Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Anamara Ritt-Olson, Joel Milam, Lilibeth Torno, Gillian R. Hayes
When Worlds Collide: Boundary Management Of Adolescent And Young Adult Childhood Cancer Survivors And Caregivers, Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Anamara Ritt-Olson, Joel Milam, Lilibeth Torno, Gillian R. Hayes
Engineering Faculty Articles and Research
Adolescent and young adult childhood cancer survivors experience health complications, late or long-term biomedical complications, as well as economic and psychosocial challenges that can have a lifelong impact on their quality-of-life. As childhood cancer survivors transition into adulthood, they must learn to balance their identity development with demands of everyday life and the near- and long-term consequences of their cancer experience, all of which have implications for the ways they use existing technologies and the design of novel technologies. In this study, we interviewed 24 childhood cancer survivors and six caregivers about their cancer survivorship experiences. The results of our …
Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen
Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen
Engineering Faculty Articles and Research
Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …
Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead
Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead
Engineering Faculty Articles and Research
Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …