Health Information Technology Commons^™

Temporal Modeling And Forecasting Of Blood Glucose Dynamics In Individuals With Diabetes Mellitus, Mj Ruff

University Honors Theses

People living with Diabetes Mellitus face significant health risks, including an increased likelihood of heart disease, stroke, and fluctuations in blood glucose levels. The unpredictable nature of glucose levels can lead to dangerous conditions such as ketoacidosis and hypoglycemia. This study employs advanced time series analysis tools to forecast the glucose levels for an individual diagnosed with Type 1 Diabetes Mellitus.

Forecasting Influenza Rates Using Machine Learning: A Study Of Chatgpt's Predictive Accuracy, Sara Saleh

University Honors Theses

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall …

Explainable Ai (Xai) For A Machine Learning Heart Disease Prediction Model, Sai Abhishek Sanchula

Electronic Theses, Projects, and Dissertations

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating the development of accurate and interpretable machine learning (ML) models for early diagnosis and risk assessment (World Health Organization, 2021). While ML algorithms such as logistic regression, decision trees, support vector machines (SVM) (Cortes & Vapnik, 1995), and deep learning models (LeCun et al., 2015) have demonstrated high predictive accuracy, their adoption in clinical practice is hindered by their black-box nature (Rudin, 2019). Explainable AI (XAI) techniques, including SHapley Additive Explanations (SHAP) (Lundberg & Lee, 2017), Local Interpretable Model-agnostic Explanations (LIME) (Ribeiro et al., 2016), and feature importance analysis …

Data-Driven Survival Modeling For Breast Cancer Prognostics: A Comparative Study With Machine Learning And Traditional Survival Modeling Methods, Theophilus Gyedu Baidoo, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes.

Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) …

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

Ransomware In Healthcare: Threats, Impacts, And Mitigation Strategies, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

Excerpt: The growing digitalization of healthcare has exposed hospitals to significant cybersecurity threats, particularly ransomware attacks. The Health Sector Cybersecurity Coordination Center (HC3) reported that as of mid-2024, there were 730 cyber-attacks worldwide against healthcare institutions, with 530 targeting the U.S. (AHA, 2024). Half of these incidents involved ransomware, a type of malware that restricts access to critical data until a ransom is paid (HHS, 2021). Hospitals are attractive targets for cybercriminals due to their essential role in patient care. Cybercriminals exploit vulnerabilities in hospital networks, often causing severe operational and financial damage. Factors such as understaffed IT teams, outdated …

Unlocking Precision Using K-Means++- Improved Genetic Algorithm-Radial Basis Function Neural Network: Data-Driven Evolution Of Smart Gloves For Gesture Recognition, Liang Xiao Ding, Kuan Way Chee, Hong Lü, Anand Paul, Jeonghong Kim, Jang Myung Lee

School of Public Health Faculty Publications

Human-computer interaction technologies have been used since the 1970s but have only gained growing popularity in recent years with new design paradigms. Ongoing research and development in gesture recognition systems with broad application prospects have focused on improving accuracy and real-time performance as well as the robustness of specific machine learning algorithms against environmental conditions. This paper addresses the accuracy enhancement of a novel Fifth Dimension Technologies data-glove-based gesture recognition system using a genetic-algorithm (GA)-trained k-means++-improved radial basis function (RBF) or GK-RBF neural network. First, we analyzed and modeled the sensor distribution in the data glove and proposed joint constraints …

A Deep Sparse Capsule Network For Non-Invasive Blood Glucose Level Estimation Using A Ppg Sensor, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Emad Muteb Alharbi, Hibah Qasem Salman Alatawi, Kousalya Prabahar, Jawhara Bader Aljabri, Anand Paul

School of Public Health Faculty Publications

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving …

Preserving Privacy In Senior Care At Home Monitoring Systems, Sam Russel, Ryan Benton, Amy Campbell, Scott Sittig

Shelby Hall Graduate Research Forum Posters

Many seniors prefer to live at home which necessitates research into the application of technologies to provide a safer environment with less caregiver resources. However, the application of home health care (HHC) monitoring for seniors is still in an evolutionary stage. Present HHC systems are produced by private companies with general regulatory guidelines lacking specific care of the elderly. As such, each company that produces such a system claims to have better safety, privacy, and security that their competitors. A pressing issues is devising and applying a general framework for the application of technologies that delivers safety while preserving privacy. …

False Narratives, Real Consequences, Russell W. Cantrell, Matt Campbell

Shelby Hall Graduate Research Forum Posters

Social media is an increasingly significant tool in modern cyber warfare, capable of rapidly shaping public opinion. The swift dissemination of information complicates efforts to distinguish fact from fiction [1]. During public health crises, healthcare professionals use these platforms to share updates, yet their credible content must contend with false or deliberately misleading narratives [2]. This environment creates an opportunity for cyberattacks through social media influence campaigns [3]. While disinformation's role in political interference has been widely studied, its potential to destabilize healthcare remains largely unexplored. Prior research primarily focuses on how vaccine misinformation affects the general public [4]. This …

Investigating The Relationship Between The Circadian Clock, Age, And Lifestyle, Samuel Park, Amaka Eziakonwa, Amega Nguyen, Madison Payne, Erich Stahmer, Alexis Salinas, Payson Broome, Angel Oliver, Christian Mccormick, Jasmyn Brown, Julie A. Zacharias-Simpson, Jutta Ward, Deborah Bell-Pedersen

Annual Research Symposium

This poster presentation aims to understand how circadian clock strength and phase are affected by age and lifestyle. By analyzing changes in physiological rhythms, the study seeks to develop strategies for promoting healthy aging by optimizing circadian function.

Tracking The Effects Of Time Zone Shift On An Individual’S Circadian Rhythm, Amaka Eziakonwa, Erich Stahmer, Amega Nguyen, Samuel Park, Madison Payne, Alexis Salinas, Deborah Bell-Pedersen, Julie A. Zacharias-Simpson

Annual Research Symposium

This poster presentation aims to understand how a rapid time-zone change can affect an individual’s circadian rhythms. By quantifying changes in physiological rhythms, the study seeks to develop targeted therapeutic interventions to mitigate the negative effects of jetlag.

Can We Eliminate The Negative Consequences Of Circadian Desynchronization Caused By Night Shift Work By Using A Light Therapy Lamp To Simulate A Sunset?, Amega Nguyen, Madison Payne, Amaka Eziakonwa, Erich Stahmer, Alexis Salinas, Samuel Park, Julie A. Zacharias-Simpson, Deborah Bell-Pedersen

Annual Research Symposium

This poster aims to understand the relationship between desynchronosis of circadian rhythms and night-shift work and how light therapy can mitigate some of the issues of desynchronosis. This study seeks to develop therapies for disrupted circadian rhythms from night-ship works.

Comparing In-Person, Standard Telehealth, And Remote Musculoskeletal Examination With A Novel Augmented Reality Exercise Game System: Pilot Cross-Sectional Comparison Study, Richard Wu, Keerthana Chakka, Sara Belko, Ninad Khargonkar, Kevin Desai, Balakrishnan Prabhakaran, Thiru Annaswamy

SKMC Student Presentations and Publications

BACKGROUND: Current telemedicine technologies are not fully optimized for conducting physical examinations. The Virtual Remote Tele-Physical Examination (VIRTEPEX) system, a novel proprietary technology platform using a Microsoft Kinect-based augmented reality game system to track motion and estimate force, has the potential to assist with conducting asynchronous, remote musculoskeletal examinations.

OBJECTIVE: This pilot study evaluated the feasibility of the VIRTEPEX system as a supplement to telehealth musculoskeletal strength assessments.

METHODS: In this cross-sectional pilot study, 12 study participants with upper extremity pain and/or weakness underwent strength evaluations for four upper extremity movements using in-person, telehealth, VIRTEPEX, and composite (telehealth plus VIRTEPEX) …

Vaxbot-Hpv: A Gpt-Based Chatbot For Answering Hpv Vaccine-Related Questions, Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

METHODS: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and …

Using Nanotechnology To Achieve Sustainability In Interior Residential Spaces, Neveen Youssef Azmy, Esraa Tarek Abu-El Azm, Ahmed Ahmed Rizk, Walaa Abou El-Haggag Mehanna

Journal of Engineering Research

Major scientific and technological developments characterize the present time. One notable achievement is the nano revolution, which has greatly influenced various areas of life Nanotechnology is characterized by using very small particles of materials, either alone or by processing them to create new materials with remarkable properties. Hence, nanotechnology offers promising economic solutions and products to achieve a reliable and sustainable environment. Architecture has great potential to benefit from this technology to achieve sustainability and improve indoor environment quality. Given the proliferation of residential buildings in urban areas, where people spend most of their time, it is necessary to integrate …

Evaluating Lidar Technology In Iphone Mobiles For 2d Mapping Applications, Nasr Mohammady Saba, Salem Salama Saleh

Journal of Engineering Research

Abstract- LiDAR (Light Detection and Ranging) technology has revolutionized mapping applications, offering enhanced precision and accuracy in spatial data collection. The integration of LiDAR technology in the latest iPhone models, beginning with the iPhone 12 Pro, has introduced new capabilities for 2D data measurement and plan creation directly on the mobile device. The iPhone 12 Pro and 12 Pro Max models support LiDAR techniques within their mobile phone cameras, enhancing the performance of various applications such as Measurements and Photos. LiDAR operates by emitting pulsed light waves into the surrounding space, which bounce off nearby objects and return to the …

Experimental And Numerical Investigation For Optimum Design Of Hydraulic Steel Gates, Basma Hassan Mohamed Eng, Ahmed Mohamed Anwer Dr, Hanan Eltobgy Prof., Gamal Helmy El-Saeed Prof

Journal of Engineering Research

Conventionally, steel gates are widely used in hydraulic structures for their superior mechanical properties in sustaining hydrostatic pressure. Maintenance and operational costs should be considered while studying the feasibility of these gates. In the current research, three prototype steel gates with different configurations were prepared and examined experimentally. The three gates have the same surface dimensions of (width x height) equal to (1.0 x 1.25 m). The first steel gate represents a typical traditional gate made of skin plate and stiffened laterally using standard steel channels and will be kept for comparison. The second gate represents a standard thicker plate …

Performance And Emissions Characteristics Of Multi-Cylinder Direct Injection Diesel Engine Fuelled With Diesel/Biodiesel And Toluene Additives, Hagar Alm-Eldin Bastawissi, Medhat Elkelawy Prof. Dr. Eng., Mohammed Osama Elsamadony Dr. Eng., Moustafa Ghazaly Eng.

Journal of Engineering Research

This study explores the impact of using a diesel, biodiesel, and toluene additive fuel blend in a multi-cylinder direct injection diesel engine, focusing on both performance and emissions characteristics. Biodiesel, made from renewable sources like vegetable oils, is often added to diesel to reduce reliance on fossil fuels and improve combustion due to its higher oxygen content. In this work, a 45% biodiesel blend (B45) led to significant reductions in particulate matter (PM) emissions by up to 40% compared to conventional diesel. Additionally, carbon monoxide (CO) and hydrocarbon (HC) emissions decreased by up to 30% and 25%, respectively, due to …

Effect Of Dual-Fuelled Cng And Gasoline On Spark Ignition Engine Performance And Emissions Behaviors At Different Loads, Medhat Elkelawy Prof. Dr, Eng., M.M. Bassuoni Prof. Dr., Hagar Alm-Eldin Bastawissi, Saied I. Haiba Eng.

Journal of Engineering Research

Engine emissions are one of numerous factors that have detrimentally affected the environment, such as global warming, which is the result of growing exhaust gases, practically carbon dioxide (CO2). These emissions have substantially influenced researchers to develop strategic initiatives to minimize the carbon contents of fuels. The purpose of this research is to investigate the characteristics of internal combustion (SI) engines fueled by gasoline and compressed natural gas (CNG) blends. The experiments are performed using a HONDA 4-stroke, single-cylinder, and air-cooled SI engine. Five CNG concentrations are evaluated, the concentrations range from 0.5 to 2.5 L/min with a 0.5 L/min …

A Quantitative Analysis Of The Commercial-Additive Effects On Diesel Engine Combustion And Emissions Characteristics, Medhat Elkelawy Prof. Dr. Eng., Hagar Alm-Eldin Bastawissi Prof. Dr. Eng., E. A. El Shenawy Prof. Dr., Mahmoud Mohamed Soliman Mahmoud Mohamed Mahmoud Soliman

Journal of Engineering Research

Petroleum fuel prices fluctuate in response to global political and economic issues. Government regulations should be followed when it comes to engine emissions, as these dangerous pollutants have a negative impact on the environment. One such effect is global warming, which raises global temperatures. The purpose of this study is to develop the additives for use as a fuel mix with diesel fuel, which will be used to feed the diesel engine of a single cylinder, four strokes. The engine will be tested experimentally with varying loads at fixed speed of 1500 rpm, using blends of [(5% PRETSONE additive + …

Design And Implementation Of A Low-Cost Educa-Tional Robot For Engineering Curriculums, Diana Refaat Henry Jacob, Ashraf Mohamed Hafez, Mohamed Tarek Elawa

Journal of Engineering Research

The role of educational robots is becoming increasingly important. Many commercial educational robots differ in their abilities and price. In general, the cost of these robots presents the main cause of their limited use in the teaching process, especially in countries with limited financial resources. This work attempts to solve this problem by designing and implementing an educational robot with a limited budget. The robot was initially intended to serve higher education students' needs, especially in engineering curriculums but can be extended to other application areas. This work presents robot hardware functional blocks, mechanical struc-ture design, and 3D printing implementation. …

A Greening The Diesel: Vegetable Oil Biodiesel Blends For Cleaner Emissions And Improved Direct Injection Diesel Engine Performance, Medhat Elkelawy Prof. Dr, Eng., Hagar Alm-Eldin Bastawissi, E. A. El Shenawy Prof. Dr., Mustafa Mohamed Ouda

Journal of Engineering Research

In this study, we conducted comprehensive experiments to assess and compare the performance of pure diesel fuel against biodiesel produced from corn oil in various blend ratios (15/85, 30/70, 45/55, and 60/40) in a conventional four-stroke, direct-injection diesel engine, fuel is injected directly into the combustion chamber at the end of the compression stroke, allowing for efficient combustion and power generation over four distinct phases: intake, compression, power, and exhaust. Each fuel blend was tested at an engine speed of 16,000 rpm across different load conditions (low, medium, and high). Key performance metrics, including volumetric fuel consumption, exhaust smoke, and …

A Spine-Specific Lexicon For The Sentiment Analysis Of Interviews With Adult Spinal Deformity Patients Correlate With Sf-36, Sf-36, And Odi Scores: A Pilot Study Of 25 Patients, Ross Gore, Michael M. Safaee, Christopher J. Lynch, Christopher P. Ames

VMASC Publications

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis …

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Synthetic Data Generation Of Health And Demographic Surveillance Systems Data: A Case Study In A Low- And Middle-Income Country, Dorcas G. Mwigereri, Nigel T. Kamotho, Akbar K. Waljee, Ryan T. Rego, Eileen M. Weinheimer-Haus, Farhana Alarakhiya, Anthony K. Ngugi, W. Nicholson Price, Ji Zhu, Stephen Peter Wong, Geoffrey H. Siwo

Articles

Objective: To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and Methods: Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/ Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results: CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving …

Label Correlated Contrastive Learning For Medical Report Generation, Xinyao Liu, Junchang Xin, Bing Tian Dai, Qi Shen, Zhihong Huang, Zhiqiong Wang

Research Collection School Of Computing and Information Systems

Background and Objective: Automatic generation of medical reports reduces both the burden on radiologists and the possibility of errors due to the inexperience of radiologists. The model that utilizes attention mechanism and contrastive learning can generate medical reports by capturing both general and specific semantics. However, existing contrastive learning methods ignore the specificity of medical data, that is, a patient may suffer from multiple diseases at the same time. This means that the lack of fine-grained relationships for contrastive learning will lead to the problem of insufficient specificity. Methods: To address the above problem, a label correlated contrastive learning method …

Predicting Mental Health Disparities Using Machine Learning For African Americans In Southeastern Virginia, Ismail El Moudden, Michael C. Bittner, Matvey V. Karpov, Isaac O. Osunmakinde, Akosua Acheamponmaa, Breshell J. Nevels, Mamadou T. Mbaye, Tonya L. Fields, Karthiga Jordan, Messaoud Bahoura

Department of Obstetrics & Gynecology Faculty Publications

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. …

The Reliability Gap: How Traditional Search Engines Outperform Artificial Intelligence (Ai) Chatbots In Rosacea Public Health Information Quality, Houston C. Nelson, Morgan T. Beauchamp, April A. Pace

Department of Medicine Faculty Publications

Background: The internet has become a primary source of health information for the public, with important implications for patient decision-making and public health outcomes. However, the quality and readability of this content vary widely. With the rise of generative artificial intelligence (AI) tools such as ChatGPT and Gemini, new challenges and opportunities have emerged in how patients access and interpret medical information.

Objective: To evaluate and compare the quality, credibility, and readability of consumer health information provided by traditional search engines (Google, Bing) and generative AI platforms (ChatGPT, Gemini) using three validated instruments: DISCERN, JAMA Benchmark Criteria, and Flesch-Kincaid Readability …