Medicine and Health Sciences Commons^™

Developing Prediction Models For Kidney Stone Disease, Joseph Palko

Honors Theses

Kidney stone disease has become more prevalent through the years, leading to high treatment cost and associated health risks. In this study, we explore a large medical database and machine learning methods to extract features and construct models for diagnosing kidney stone disease.

Data of 46,250 patients and 58,976 hospital admissions were extracted and analyzed, including patients’ demographic information, diagnoses, vital signs, and laboratory measurements of the blood and urine. We compared the kidney stone (KDS) patients to patients with abdominal and back pain (ABP), patients diagnosed with nephritis, nephrosis, renal sclerosis, chronic kidney disease, or acute and unspecified renal …

Investigations Involving Mononuclear And Dinuclear Transition Metal Catalysts For Photochemical Carbon Dioxide Reduction, Ansu Edwards

Honors Theses

There is currently a global energy crisis, which is in desperate need of solutions. New energy sources are required that will not pollute as much as our longstanding reliance on nonrenewable fossil fuels as an energy source. This pollution involves large amounts of greenhouse gas emissions, predominantly carbon dioxide (CO₂), that contribute to environmental problems such as climate change. In this context, a fairly recent research direction to address this problem is the development of transition metal catalysts that can convert CO₂ into reduced carbon products that can serve as chemical fuels. This work focuses on the …

The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist

Honors Theses

Technology has played an immense role in the evolution of healthcare delivery for the United States and on an international scale. Today, perhaps no innovation offers more potential than artificial intelligence. Utilizing machine intelligence as opposed to human intelligence for the purposes of planning, offering solutions, and providing insights, AI has the ability to alter traditional dynamics between doctors, patients, and administrators; this reality is now producing both elation at artificial intelligence's medical promise and uncertainty regarding its capacity in current systems. Nevertheless, current trends reveal that interest in AI among healthcare stakeholders is continuously increasing, and with the current …

Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti

Honors Theses

Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical …

Defying The Darkness: Countering Cancer With Light, Travis Hankins

Honors Theses

Triple-Negative Breast Cancer (TNBC) accounts for upwards of 15% of reported breast cancer cases. This subtype of breast cancer poses a greater threat to those diagnosed as compared to other types of breast cancer due to the lack of treatment options available. Additionally, TNBC grows and spreads faster, tends to be more aggressive, and has a greater chance of recurrence than its counterparts. Altogether, TNBC cases generally have a worse prognosis over other types of breast cancer. Photodynamic therapy (PDT) is currently being researched as a way to treat TNBC. Photodynamic therapy agents are light-activated materials used for localized disease …

Restoration Agriculture In Louisiana: On The Prospects And Ethics Of Creating A Permanent, Dynamic Agricultural System Suited For Louisiana's Environment, Noah Willsea

Honors Theses

No abstract provided.

Modeling Of Covid-19 Utilizing Various Compartmental Models To Predict Infection Rates Throughout Michigan, Colleen M. Staniszewski

Honors Theses

Compartmental modeling is a method of employing math to create a visual representation of a disease interacting with a select population, typically used in epidemiology analyses. This project applies compartmental modeling equations to data collected on the various aspects of COVID-19 in Michigan. Comparing current data to past predictive models, as well as the visual representations that were developed through the various compartmental modeling methods, allows an assessment of the effects of the preventative measures taken by the state, the various rates at which the infection is able to spread, as well as the potential path and spread of the …