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Crowding Reduction and Waiting Time Analysis in Health-Care System Using Machine Learning
No full textIn the hospital setting, the emergency room (ER) offers timely emergency care for patients and is considered the busiest department because of the urgency of cases. Emergency rooms have the highest number of patients overcrowding within any hospital; more than 50 of the patients admitted to the hospital come through the ER. Healthcare management is continuously trying to minimize wait times and optimize the hospitals allocated resources, but most ERs still suffer from the overcrowding crisis due to the stochastic arrival and random arrival distribution. Advanced techniques, such as machine learning algorithms, are useful for determining real life queue scenarios and patient flow (e.g., waiting time in queue and length of stay), which are considered measures of ER overcrowding. As such, we began by building a model to predict patient length of stay through predictive input factors such as patient age, mode of arrival, and patient’s type of condition using three machine learning algorithms (e.g., artificial neural networks (ANN), linear regression, and logistic regression). The best model accuracy ANN resulted in an increase of 19.5 compared to the performance from previous studies. Then, the Deep Learning Model was applied for historical queueing variables to vi predict patient waiting time in a system alongside, or in place of, queueing theory (QT). Four optimization algorithms (SGD, Adam, RMSprop, and AdaGrade) were applied and compared to find the best model with the lowest mean absolute error. The results showed that the SGD algorithm achieved better prediction accuracy than the traditional approach and reduced the use of assumptions. Moreover, the model decreased the error reduction by 24 when compared to prior literature. Lastly, we proposed a model to predict the patient waiting time based on the lab test results. Multi-algorithms were implemented by using real-life COVID-19 test results data recorded during the pandemic. Among the eight proposed models, the results showed that decision tree regression performed better for predicting waiting times. Based on experiments performed in the research, this dissertation provides a guideline for waiting time analysis in the queue—not only in healthcare, but also in other sectors, considering model understandability and the feature extraction process
The Perspectives Of El Parents On The Family-School Partnership During Remote Learning
No full textCOVID-19 impacted the entire world, including the partnership between English learners’ (EL) families and schools. The purpose of the study was to analyze and evaluate the impact of remote learning on EL families and how family-school partnerships were enabled or disabled in the remote learning environment. In this study a questionnaire was distributed to EL parents via social-media, 80 of which met the criteria and completed the survey. An embedded, mixed method, research design was used. SPSS was used to evaluate the quantitative data using descriptive statistics, one-way ANOVAs, and t-tests. Blending, including deductive and inductive coding, was used to evaluate the qualitative data. Findings indicated communication and helping students learn at home had the highest average of means, followed by Parenting, Volunteering, and Decision-Making. The findings also indicated that there was no significant statistical difference in the survey responses for parent’s education, employment status, years living in the United States, English language level, and EL parent meeting attendance in relation to family-school partnerships during remote learning. Additional findings revealed technology assistance, non-technical supports, the role of parents, and teaching children responsibility helped EL parents feel supported in their efforts to be engaged. Furthermore, technology issues, instructional issues, culture and language barriers, socioemotional issues, and parental challenges were identified as perceived challenges reported by EL families in supporting their children to learn at home while engaged in remote learning. Finally, findings indicated schools’ provision of basic needs, technical and instructional support, communication, and planning assistance were ways parents felt most supported in their partnership with their children’s school amidst COVID-19. These findings provide valuable insight to policy-makers, administrators, Department of Education and educators, as they can use the information to understand the resources EL families need to effectively partner with their children’s school during times of remote learning. This information can provide additional insight so that curriculum developers in higher education and continuing education can review and refine curriculum to address any areas in which additional support may be needed to ensure that home school partnerships with EL families maximize learning opportunities for EL students
Novel Piezoelectric Biosensor Based on SARS-CoV-2 (COVID-19) Spike Antibody for Coronavirus (Covid-19) Detection
No full textAt the end of December 2019, the novel coronavirus SARS‐CoV‐2 appeared in Wuhan, China. The World Health Organization released a global health emergency declaration based on growing case notification rates in several locations worldwide. Therefore, sensitive, specific, rapid, and deliverable diagnostic monitoring is vital for making proper decisions on treating and isolating infected patients, which will help prevent the spread of infectious diseases. The surface Acoustic Wave (SAW) biosensor provides a unique, highly sensitive electrical approach to biomolecule detection and cell growth. For this study, a novel SAW sensor is developed, and the mass sensitivities are tested to detect the SARS‐CoV‐2 by attaching the SARS-CoV-2 spike antibody immobilized on the sensor surface. First, a two-dimensional (2D) and a three-dimensional (3D) finite element model were developed based on a realistic device to obtain a complete characterization of the senor. Then, the AlN/Al2O3 fabricated sensor was tested and ultrasonically rinsed in preparation for silanization. After depositions of (APTMS) on the sensor by the Chemical Vapor Deposition method, the antibodies were immobilized on surfaces with the aid of a crosslinker (EDC) and (Sulfo-NHS). Finally, the SARS-CoV-2 was introduced to the sensor, and the attachment of the immobilized antibody was tested and evaluated. The sensor was tested and characterized by Raman spectroscopy and the vector Network Analyzer. Finally, our device was able to detect the virus in real-time time (within two to three minutes), confirming its high sensitivity and selectivity with regard to the SARS-CoV-2 virus
Oakland University Alumni Magazine, Fall 2021
No full textDefining moments at OU: The mentors, classes and achievements that helped shape our alumni’s futures ; Homecoming and reunion weekend ; Full Circle: Two friends journey from being on academic probation to aiding those in similar situations ; In the gold seat with Annie Meinberg ; Seize the day: OU alumnus shares his experience bicycling cross-country at the age of 70 ; A-Major influence: OU legacy student turns family passion into career ambitions ; A path forward ; Branching out ; Finding common ground: How OU taught one alumnus to prioritize trust and understanding in politics ; Campus highlight
