Data Science Commons^™

[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.

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.

Validation Of A Risk Score For Cancer-Associated Thrombosis Using Nationwide Ehr Data, Ang Li, Omid Jafari, Barbara D Lam, Jun Y Jiang, Rock Bum Kim, Shengling Ma, Emily Zhou, Joyce W Tiong, Elizabeth C Chiang, Justine Ryu, Christopher I Amos, Jennifer La, Nathanael R Fillmore

Faculty, Staff and Student Publications

Importance: Venous thromboembolism (VTE) is associated with increased mortality and morbidity in patients with cancer. Existing risk prediction models are typically validated within individual sites, a fragmented approach that limits clinical adoption.

Objective: To validate the electronic health record cancer-associated thrombosis (EHR-CAT) score compared with the benchmark Khorana score in a contemporary cohort of patients with cancer across the nation, before and after treatment, excluding those at high risk of bleeding.

Design, setting, and participants: This prognostic study included patients in a nationwide longitudinal EHR database from January 2018 to December 2023 with follow-up continuing to April 2025. Patients with …

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

The Science Of Sound: Studying The Cognitive, Emotional, And Physiological Effects Of Frequency, Genre, And Music, Jett Yarborough

Senior Honors Theses

Music influences emotion, physiology, and cognition, yet little is known about how the frequency it is tuned to affects these influences. Prior research has shown that music tuned to 432 Hz can reduce stress, lower blood pressure, and improve sleep. My colleagues and I conducted two studies to investigate this. Our research found that music tuned to 432 Hz promotes a significant increase in memory retention and induces a state of focus and relaxation. These results suggest that shifting from the standard tuning frequency of 440 Hz to 432 Hz could have a profoundly positive impact on our daily lives. …

A Modern Analytical Method To Forecast Cerebrovascular Diseases (Cd), And Heart Diseases (Hd) Using Multivariate Time Series Model Utilizing The Cdc Provisional Mortality Data, Aditya Chakraborty, Mohan Pant

Cardiovascular Research Symposium

Background: In this study, a new analytical approach was introduced to answer specific questions related to mortalities due to cerebrovascular diseases, heart diseases, and the association of these mortalities with twelve other causes of death (COD).

Methods: A multivariate time series forecasting model was developed utilizing each of the CODs by taking the weekly and yearly seasonality into account, and the mortality counts were forecasted using the most recent CDC weekly mortality count data. A new COD data matrix was structured for all CODs as a function of weeks by combining the observed and predicted values of the mortality counts. …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Tackling Data Quality Challenges In Remote Sensing: Solutions For Reliable Urban Heat Island Analysis, Wei Xia, Aqil Tariq, Hesham El-Askary, Rana Waqar Aslam, Elgar Barboza, Dmitry E. Kucher, Youssef M. Youssef, Habib Kraiem

Mathematics, Physics, and Computer Science Faculty Articles and Research

Urban heat islands (UHIs) pose critical challenges to public health, energy demand, and environmental sustainability, particularly in rapidly expanding urban regions. This study examines the complex relationship between building configurations and integrated green spaces, as well as their combined impact on thermal regulation. It focuses on addressing data quality issues commonly encountered in remote sensing applications. Using high-resolution multispectral and thermal imagery, we developed an integrated modeling approach that captures the collective influence of built form and green infrastructure on urban microclimates. A key finding is the significant linear inverse relationship between green space coverage and land surface temperature, underscoring …

A Cancer Education Needs Assessment: Informing Middle-Aged Female Patients About The Relationships Between Obesity And Women’S Health Concerns In The Reproductive System, Breast, And Endometrial Health, Batul Mirza

MUSC Theses and Dissertations

Obesity significantly impacts women’s health, particularly among middle-aged women, by increasing the risk of hormone-sensitive cancers such as breast, endometrial, and reproductive system cancers. This study examines the educational needs of this demographic group regarding obesity-related cancer risks and explores effective intervention strategies. Obesity-induced mechanisms – hormonal imbalances, chronic inflammation, and insulin resistance – drive cancer susceptibility, emphasizing the need for targeted health education. The study employs a qualitative design, which includes interviews with subject matter experts (SMEs) and surveys of middle-aged women. The goal is to assess awareness, perceived barriers, and preferred learning methods. Findings suggest that with many …

Modeling, Analysis, And Prediction Of Covid-19 Dynamics With Interacting Subpopulations And Implicit Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer

Spora: A Journal of Biomathematics

In this paper, we consider an extended SEIR compartmental model that incorporates young and old interacting subpopulations, allowing for cross-group transmission dynamics. Implicit behavioral changes are included to determine the influence of social behavior on coronavirus transmission dynamics. The basic reproduction number, the average number of secondary cases of infection produced by a single primary case, is derived for both the explicit and implicit model using the next-generation matrix method. We solve the associated differential equation systems and estimate useful parameters in the explicit model using physics-informed neural networks (PINNs). Our results point to how the PINNs approach offers an …

Use Of High-Throughput Phenomics With And Without Fungicide As A Wheat Breeding Tool, Gerardo Ivan Rivera Collazo

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Wheat (Triticum aestivum) is an important food staple for many countries around the world and current consumption and production data demonstrate a production deficit. Breeders are challenged to help close this gap in production by selecting better cultivars with improved yields. Several methods aim to accelerate the breeding process to reduce the time required for releasing an elite variety. One of the main breeding bottlenecks is the lack of fast and reliable phenotypic data acquisition that could dissect physiological and morphological plant data. High-throughput remote sensing could have the potential to reduce this bottleneck by streamlining data acquisition, …

Early Diagnosis And Detection Of Skin Cancer Using Deep Neural Network Models And Feature Extraction From Pre-Trained Cnns, Sahil Khanna

Harrisburg University Dissertations and Theses

Skin cancer is one of the most common and lethal cancer types. While accurate diagnosis at an early stage is essential for skin cancer treatment it remains difficult to achieve in many regions due to lack of sufficient dermatologists and proper diagnostic equipment. Prior studies show Convolutional Neural Network (CNN) models excel at skin lesion classification and consistently achieve better results than standard diagnostic practices. However, the focus of many studies remains confined to image-based learning while neglecting useful patient metadata that could improve prediction accuracy. This research project created a specialized CNN model to classify skin lesions and evaluated …

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.

Detecting Physical Activity Using Wearable Sensor Data, Dipok Deb

Data Science and Data Mining

This study focuses on detecting physical activity using wearable sensor data, specifically distinguishing between walking and running. A dataset comprising accelerometer and gyroscope readings is used to train and evaluate various machine learning models, including logistic regression, random forest, k-nearest neighbors, naïve Bayes, and XGBoost. Extensive preprocessing, such as creating lag features and rolling statistics, is performed to enhance temporal data representation. The models are evaluated using metrics like accuracy, precision, recall, and F1 score. Incorporating lag and rolling features significantly improves model performance, with logistic regression achieving perfect scores across all metrics. These findings demonstrate the effectiveness of enhanced …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Transforming The Future Of Health: Building Learning Health Systems Across The Globe, Sandra Yankah, Robert Saunders, Mark L. Tykocinski, Claudia Salzberg, Jonathan Gonzalez-Smith, Rachel Bonesteel, Cameron Joyce, Charles Kahn, Mark Mcclellan, Eyal Zimlichman

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Health care has faced disruptions over the past 5 years, including a global pandemic, supply chain interruptions, workforce shifts, and the introduction of new artificial intelligence (AI) tools. Health care organizations continue to leverage the learning health system (LHS) concept to adapt to these challenges through iterative feedback loops. The Future of Health (FOH), an international community of over 50 senior health leaders that focuses on shared challenges across international health systems, collaborated with the Duke-Margolis Institute for Health Policy in a consensus-building process with FOH members to identify opportunities for action in an LHS. Key areas for action identified …

Computerized Diagnostic Decision Support Systems-Isabel Pro Versus Chatgpt-4 Part Ii, Joe M Bridges, Xiaoqian Jiang, Michael Ige, Oluwatoniloba Toyobo

Faculty, Staff and Student Publications

Objective: Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?

Materials and methods: Isabel Pro, a computerized diagnostic decision support system, and ChatGPT-4, a large language model. Using 201 cases from the New England Journal of Medicine, each system produced a differential diagnosis ranked by likelihood. Statistics were Mean Reciprocal Rank, Recall at Rank, Average Rank, Number of Correct Diagnoses, and Rank Improvement. For reproducibility, the study compared the initial expert panel run …

Histone Methyltransferase Ash1l Primes Metastases And Metabolic Reprogramming Of Macrophages In The Bone Niche, Chenling Meng, Kevin Lin, Wei Shi, Hongqi Teng, Xinhai Wan, Anna Debruine, Yin Wang, Xin Liang, Javier Leo, Feiyu Chen, Qianlin Gu, Jie Zhang, Vivien Van, Kiersten L Maldonado, Boyi Gan, Li Ma, Yue Lu, Di Zhao

Faculty, Staff and Student Publications

Bone metastasis is a major cause of cancer death; however, the epigenetic determinants driving this process remain elusive. Here, we report that histone methyltransferase ASH1L is genetically amplified and is required for bone metastasis in men with prostate cancer. ASH1L rewires histone methylations and cooperates with HIF-1α to induce pro-metastatic transcriptome in invading cancer cells, resulting in monocyte differentiation into lipid-associated macrophage (LA-TAM) and enhancing their pro-tumoral phenotype in the metastatic bone niche. We identified IGF-2 as a direct target of ASH1L/HIF-1α and mediates LA-TAMs' differentiation and phenotypic changes by reprogramming oxidative phosphorylation. Pharmacologic inhibition of the ASH1L-HIF-1α-macrophages axis elicits …

Incorporating Latent Survival Trajectories And Covariate Heterogeneity In Time-To-Event Data Analysis: A Joint Mixture Model Approach, Fu-Wen Liang, Wenyaw Chan, Michael D Swartz, Bouthaina S Dabaja

Faculty, Staff and Student Publications

Background: Finite mixture models have been recently applied in time-to-event data to identify subgroups with distinct hazard functions, yet they often assume differing covariate effects on failure times across latent classes but homogeneous covariate distributions. This study aimed to develop a method for analyzing time-to-event data while accounting for unobserved heterogeneity within a mixture modeling framework.

Methods: A joint model was developed to incorporate latent survival trajectories and observed information for the joint analysis of time-to-event outcomes, correlated discrete and continuous covariates, and a latent class variable. It assumed covariate effects on survival times and covariate distributions vary across latent …

Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi

Open Educational Resources

This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.

Two-Sample Bi-Directional Causality Between Two Traits With Some Invalid Ivs In Both Directions Using Gwas Summary Statistics, Siyi Chen

School of Public Health Faculty Publications

Mendelian randomization (MR) is a widely used method for assessing causal relationships between risk factors and outcomes using genetic variants as instrumental variables (IVs). While traditional MR assumes uni-directional causality, bi-directional MR aims to identify the true causal direction. In uni-directional MR, invalid IVs due to pleiotropy can violate assumptions and introduce biases. In bi-directional MR, traditional MR can be performed separately for each direction, but the presence of invalid IVs poses even greater challenges. We introduce a new bi-directional MR method incorporating stepwise selection (Bidir-SW) designed to address these challenges. Our approach leverages public genome-wide association study (GWAS) datasets …

Advancing Drug-Drug Interaction Prediction Using Multi-Modal Feature Integration With Graph Neural Networks, Ernest C. Chianumba

Theses, Dissertations and Culminating Projects

Pharmaceutical treatments are essential for managing medical conditions, but drug-drug interactions (DDIs) pose significant risks to patient safety and healthcare outcomes. This research integrates Knowledge Graphs and Graph Neural Networks to predict DDIs by exploring complex drug relationships. We construct a comprehensive knowledge graph using DrugBank data (1,000 drugs, 155,774 interactions) enriched with molecular features from PubChem. Our methodology introduces a novel multi-modal approach by integrating transformer-based embeddings (ChemBERTa, SPECTER, and SBERT) to create 1152-dimensional feature vectors that capture structural, biomedical literature, and semantic properties of drugs. Formulating DDI prediction as a link prediction task, we compare three Graph Neural …

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 …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Evaluating The Causal Effect Of Receiving Transthoracic Echocardiography On 28-Day Mortality In Mimic-Iii Intensive Care Unit Patients With Sepsis, Monserrath Velez, Jack O'Connor, Nicholas Della Pesca, Rio Baliga

Research & Creative Achievement Day

❏ Sepsis (an infection associated with vital organ dysfunction) is an emergent

medical condition estimated to occur in about 30% of intensive care unit

(ICU) patients[1] and is responsible for 20% of all deaths worldwide[2]

❏ Transthoracic echocardiography (TTE; an imaging modality that uses

ultrasound technology to record heart structure and function) is widely used in

medical treatment [3]

❏ 28-day mortality (whether or not a patient dies within 28 days after receiving a

treatment) is a measure considered to closely approximate ICU mortality[4]

Objectives

❏ Assess and quantify the causal effect of receiving a TTE on …

Application For Prediction Of Heart Failure; The Next Step In Machine Learning For Healthcare, Amy Adyanthaya, Dawn Bowerman, Rachel Liercke, Robert Slater

SMU Data Science Review

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims …

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 …

Opioid Vs. Money Choice Preference Patterns In Regular Heroin Users, Amolak S. Jhand, Mark Greenwald

Medical Student Research Symposium

About two-thirds of people treated for opioid use disorder (OUD) return to opioid use within the first-year post-treatment, and about 10% report use while on agonist therapy. Understanding determinants of opioid-seeking is vital to reducing recurrence and its risks. We assessed individual differences in effortful choices between opioid and money amounts, modeling real-world choices.

Our lab conducted studies in which regular heroin-users were stabilized on buprenorphine to suppress withdrawal. Within experimental sessions, the participant could choose repeatedly across 12 trials between units of hydromorphone (HYD, 1 or 2 mg IM) vs. money ($2 or $4); HYD and money amounts differed …

Surgical Versus Nonsurgical Management Of Civilian Craniocerebral Gunshot Injuries, Wesley Shoap, George Austin Crabill, Roboan Guillen, Kaleb Derouen, Jack Leoni, Zhide Fang, Berje Shammassian

School of Medicine Faculty Publications

Introduction: Craniocerebral gunshot wounds in the civilian population constitute a devastating subset of traumatic brain injuries (TBI). The aim of this study was to determine the association of mortality, intensive care unit length of stay (ICU LOS), and the Glasgow Outcome Scale Extended (GOS-E) among craniocerebral gunshot patients based on timing and type of intervention. Methods: The trauma database was queried for GSWH patients ages 15 and older who received neurosurgical intervention from January 1st 2016 to June 1st 2023. Operative notes were reviewed and patients were then divided into three groups; intracranial pressure monitor only with medical treatment (ICP), …

Liver Tet1 Promotes Metabolic Dysfunction-Associated Steatotic Liver Disease, Hongze Chen, Muhammad Azhar Nisar, Joud Mulla, Xinjian Li, Kevin Cao, Shaolei Lu, Katsuya Nagaoka, Shang Wu, Peng Sheng Ting, Tung Sung Tseng, Hui Yi Lin, Xiao Ming Yin, Wenke Feng, Zhijin Wu, Zhixiang Cheng, William Mueller, Amalia Bay, Layla Schechner, Xuewei Bai, Chiung Kuei Huang

School of Public Health Faculty Publications

Global hepatic DNA methylation change has been linked to human patients with metabolic dysfunction-associated steatotic liver disease (MASLD). DNA demethylation is regulated by the TET family proteins, whose enzymatic activities require 2-oxoglutarate (2-OG) and iron that both are elevated in human MASLD patients. We aimed to investigate liver TET1 in MASLD progression. Depleting TET1 using two different strategies substantially alleviated MASLD progression. Knockout (KO) of TET1 slightly improved diet induced obesity and glucose homeostasis. Intriguingly, hepatic cholesterols, triglycerides, and CD36 were significantly decreased upon TET1 depletion. Consistently, liver specific TET1 KO led to improvement of MASLD progression. Mechanistically, TET1 promoted …