Medicine and Health Sciences Commons^™

Transdisciplinary Perspectives On Ai: The Fourth Annual Conference Of The European Culture And Technology Laboratory, Connell Vaughan, Ioana Madalina Moldovan, Silivan Moldovan, Noel Fitzpatrick

Books/Book Chapters

The fourth annual conference of the ECT Lab+ was hosted by Technical University of Cluj-Napoca over two days in October 2024 at the Cluj Innovation Park. The conference brought together experts from the Arts, Humanities, Social Sciences, Technology, and other fields to discuss and reflect on the advent of Artificial Intelligence and how the associated technologies are transforming how we live, work and study. Under the title Transdisciplinary perspectives on AI: Alternative Histories, Current Practices and Possible Futures the conference moved beyond simplistic technophila and technophobia to consider whether we can co-evolve with these new technologies which combine machine learning …

Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol

All Works

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …

Field Validation Of Multiple Species Distribution Models Shows Variation In Performance For Predicting Aedes Albopictus Distributions At The Invasion Edge, Anna V. Shattuck, Brandon D. Hollingsworth Ph.D., Jared Skrotzki, Scott R. Campbell, Christopher L. Romano, Courtney C. Murdock

Faculty Publications

Background

Climate and land use changes have resulted in range expansion of many species. In this shifting disease landscape, it is important to leverage tools that can predict the distributions of invading vectors to target surveillance and control efforts and identify at-risk populations. Species distribution models (SDMs) are used to predict ranges of invasive species; however, invasive species often violate assumptions of equilibrium and niche conservatism. Moreover, these studies are rarely validated using independent data.

Methods 

We use long-term surveillance data for Aedes albopictus, a highly invasive mosquito capable of transmitting several arboviruses, at its range edge to evaluate a …

Analyzing The Global Happiness Index, Victoria Hernandez, Christy W. Wachira

SMU Data Science Review

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and …

2025 - The Sixth Annual Fall Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the Fall 2025 Symposium of Student Scholars, held in November 2025. Includes abstracts from the presentations and posters.

Designing And Writing Effective Data Management Plans For Grant Proposals, Rubab Shahzad, Ibis Anette Moreno-Lozano

Day Family Research Lab Workshop Series

Fundamentals of research data management and how to create effective Data Management Plans (DMPs).

Implementation And Assessment Of The Openbci Platform As An Accessible Brain- Computer Interface, Jewell Norris

Honors Theses

OpenBCI is a low-cost, open-source platform for alternative brain-computer interface (BCI) software and hardware. This thesis evaluates OpenBCI’s electroencephalogram (EEG) and electromyography (EMG) capabilities by constructing and testing a 16-channel EEG system using the Ultracortex Mark IV headset and Cyton + Daisy biosensing board. The viability of the system was assessed through real-time BCI control and comparison to a clinical-grade EEG system. Real-time BCI control of an online falling-block game was tested via the use of eye blinks EMG (channels Fp1/Fp2) and head-tilt accelerometer inputs. The BCI game demonstrated reliable control despite minor latency and artifact sensitivity. For clinical comparison, …

Quality Assessment Of Pathology Board-Exam-Style Mcqs Produced By Chatgpt3.5: A Comparative Study, Arianna B. Morton, Zunaira Naeem, Allison F. Goldberg, Alexis R. Peedin, Joanna Chan

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Residents preparing for pathology board exams frequently use multiple-choice questions (MCQs) from question banks (QBs) like PathDojo and PathPrimer, which can be costly. ChatGPT, a free tool, has been used to generate MCQs for other tests like the SAT. This study compared the quality of pathology MCQs created by ChatGPT versus commercially available study questions for the American Board of Pathology’s (ABPath) certifying exams. A rubric adapted from the National Board of Medical Examiners’ (NBME) question writing guide was validated by two pathologists using commercially available pathology board exam questions. This rubric was then used to evaluate MCQs from commercially …

11.17.2025 Ored Connect, Liz Williamson

ORED Newsletter

• Qualtrics training sessions
• Alcorn State University, Dr. Edmund Buckner

Latent Classification Of Time-Dependent Transition Rates In Longitudinal Binary Outcome Data, Joonha Chang, Wenyaw Chan

School of Public Health Faculty Publications

Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models …

Data Cleaning With Excel, Rubab Shahzad

Day Family Research Lab Workshop Series

This workshop will delve into the importance of cleaning research data and why clean data is an important part of the larger research data lifecycle. This session will also provide an overview of data cleaning and preprocessing using Microsoft Excel including a hands-on demonstration.

Scroll down for practice file

11.10.2025 Ored Connect, Liz Williamson

ORED Newsletter

• Sherilyn Hulugalla winds Biosafety and Biosecurity Month Challenge

Shape: Spatial Health And Population Estimator, Emma M. Von Hoene, Aanya Gupta, Hamdi Kavak, Amira Roess, Taylor Anderson

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Multi-Resolution Graph Neural Networks For Spread Prediction, Petr Kisselev

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Delayed Adaptive Behavior Modulates Waves In Epidemic Models, Md Shahriar Mahmud, Solomon Eshun, Claus Kadelka, Baltazar Espinoza

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Metapopulation Model To Evaluate C.Difficile Potential Vaccine Interventions., Archana Neupane Timsina

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Novel Mathematical Model Of Oropouche Virus Transmission Dynamics, Darsh Gandhi, Carli Peterson, Emma Slack, Elizabeth Rubio, Amira Claxton, Christopher M. Kribs

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Probabilistic Modeling Of Antibody Kinetics Post Infection And Vaccination, Rayanne Luke, Prajakta Bedekar, Anthony J. Kearsley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Project Insight] [Foag] Data-Driven Machine Learning Approaches To Modeling Pertussis Vaccine Scare Behavior, Gleb Gribovskii

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mentoring First-Year Stem Students Through Collaborative Research In The Haynes Scholars Program, Alex Capaldi, Laura Tipton

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

[Lele] Measles And Mandates– What Will Happen If Florida Repeals The Mmr Vaccine Mandate?, Alice Oveson, Abba Gumel

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Machine Learning–Based Prediction Of Bleeding Risk In Factor Xi Deficiency, Tracey G. Oellerich, Stephanie Reitsma, Alisa Wolberg, Karin Leiderman, Suzanne Sindi

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bisphenol A Effects On Stress Behaviors, Alexis Galindez, Kevin Sosa, Emily Pintilie, Cassandra S. Korte

Women in Science Conference

Poster presentation given at the 2025-26 Women in Science Conference in the Christine E Lynn University Center, 2nd floor, 240-241, held on Saturday, Nov. 8, 2025.

Planarian Exposure To The Herbicide 2,4- Dichlorophenoxyacetic (2,4-D), Emily Pintilie, Alexis Galindez, Cassandra S. Korte

Women in Science Conference

Poster presentation given at the 2025-26 Women in Science Conference in the Christine E Lynn University Center, 2nd floor, 240-241, held on Saturday, Nov. 8, 2025.

[Lele] Incorporating Physiological Constraints In Estimates Of Post-Prandial Insulin Secretion Rate, Justin K. Garrish, Christine L. Chan, Douglas Nychka, Cecilia Diniz Behn

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Heterogeneity In Malaria: A Pk-Pd Immuno-Epidemiology Model With Non-Exponential Waiting Times, Katharine Gurski

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

2025-26 Women In Science Conference Schedule (Pdf), Lynn University

Women in Science Conference

No abstract provided.

Parameter Estimation And Simulation In Pollution-Induced Asthma Disease Model Using Physics-Informed Neural Networks, Aadi Gannavaram

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Sickle Cell Disease Using A Sleep-Pain-Inflammation Cycle, Milan Marsh

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Survival Analysis Of Breast Cancer Patients In Texas Using Classical And Machine Learning Methods, Sidketa Fofana, Tamer Oraby, Salique H. Shaham, Everardo Cobos, Manish K. Tripathi

School of Medicine Publications

Background

Breast cancer is considered one of the most common cancers in women worldwide. In this study, we used an 11-year cohort of malignant breast cancer survival data in Texas to investigate the factors that might explain why some breast cancer patients live longer than others.

Methods

We performed standard survival analyses, including generating Kaplan‒Meier survival curves, using the log-rank test, and applying Cox proportional hazards regression to identify the unique features of breast cancer patients and determine the main factors influencing long-term survival. We also conducted a Random Survival Forest analysis for classification and prediction. Finally, we used Mahalanobis …