Biostatistics Commons^™

Developing A Humpback Whale Vocalization Detector Using Machine Learning Models, Lucas Kantorowski

Master's Theses

Humpback whale songs are notoriously complex. Identification of humpback whale song units requires bioacousticians to tediously listen, analyze, and annotate collected sound data. Even sparse data requires listening to the entirety of the collected acoustic data. In this study, three hours of audio containing over one-thousand humpback whale song units was collected in Monterey Bay, California.

Prior studies have seen success using convolutional neural networks by performing image classification on hundreds of hours worth of spectrograms. Our study uses traditional machine learning models, as they are less computationally demanding, and require less data.

We use time splitting and Mel-frequency cepstrum …

Associational Inference With Many Potential Covariates: Bayesian Information Criterion Elastic Net, Farideh Bagherzadeh Khiabani, I-Chan Huang, Jose Miguel Martinez Martinez, Shizue Izumi, Sedigheh Mirzaei, Tiange Zheng, Irina Dinu, Yutaka Yasui

COBRA Preprint Series

Background: An emerging feature in modern biomedical research is collecting and analyzing numerous variables. In the presence of many potential covariates, inference becomes challenging requiring both distinguishing a set of covariates truly associated with an outcome and estimating their corresponding regression coefficients consistently. Traditional statistical inference typically focuses on estimating coefficients assuming a pre-specified set of covariates. Further, advanced machine/statistical learning methods performing both selection and estimation predominantly focus on outcome prediction rather than association inference.

Methods: Motivated by our epidemiological research on long-term childhood cancer survivors, where we aimed to investigate associations between a large pool of longitudinal symptom …

Beyond The Lace Index: Benchmarking Machine Learning Architectures And Explaining 30-Day Hospital Readmission Risk With Shap Analysis, Carl E. Hughes Iii

Williams Honors College, Honors Research Projects

Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, …

Spectral Analysis Of Traffic Accidents In New York's Capital Region, Michael Barr

Electronic Theses & Dissertations (2024 - present)

In this paper we estimate the spectral density of traffic accident events in the Capital Distict, NY area using a band-pass filter known as the Kolmogorov-Zurbenko Fourier Transform (KZFT). The source data is provided by Moosavi, et al. (2019) and originally captured from various public entities and sensors in the road network. Spectral density estimation with KZFT suppresses noise to reveal the constituent frequencies embedded in the noisy signal. Signal reconstruction based on KZFT produces an approximate weekly accident arrivals for this noisy signal, or in other words a pattern which is proportionate to the event expectation viewed over a …

Cross-Temporal Statistical Approaches For Evaluating Predictor-Outcome Relationships, Jeff Joseph

Dartmouth College Ph.D Dissertations

Central auditory function is linked with cognitive deficits, but few research projects use existing statistical approaches or develop new ones to forecast cognitive deficits using the results of central auditory tests. To address this limitation, we use a series of statistical learning frameworks for predicting a child’s cognitive abilities based on his/her central auditory performances and demographic factors. Two key challenges exist. First, children may start the study at a time when they are unable to perform the central auditory tests or cognitive tests. Second, cognitive performance is age-dependent, particularly in the early formative years of childhood and adolescence. To …

Iso-Detr: A Novel Detection Transformer For Industrial Small Object Detection, Faisal Saeed, Anand Paul

School of Public Health Faculty Publications

Effectively detecting and assessing real-time structural and ecological parameters in contemporary manufacturing environments poses significant challenges, particularly in identifying minute objects within product images. The swift evolution of the industrial sector underscores the necessity for intelligent manufacturing environments to uphold stringent product quality standards. However, accelerating production processes at high speeds heightens the risk of defective product outcomes. This research addresses the challenges inherent in small object detection within industrial contexts, proposing an innovative detection transformer model tailored to modern manufacturing environments. The proposed model integrates a feature-enhanced multi-head self-attention block (FEMSA), merging cross-channel communication network and multiple multi-head self-attention …

Generating Predictive Gene Expression Signatures For Alzheimer's Disease Using Postmortem Brain Tissue, Ashley Duche

Pharmaceutical Sciences (PhD) Dissertations

Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau protein aggregates. These pathological features develop in specific brain regions, but why some areas are more vulnerable to early AD-related changes remains unclear. To address this, predictive gene expression signatures were developed to explore the molecular mechanisms underlying regional susceptibility to AD pathology.

Methods: This was performed using postmortem brain (PMB) tissue from participants in the Religious Orders Study and Memory and Aging Project (ROSMAP), Mayo Clinic, and Mount Sinai Brain Bank (MSBB) to generate gene expression signatures from six brain …

A Persistent Homology Framework For Scrna-Seq: Assessing Clustering Robustness And Quantifying Preprocessing And Integration Effects On Topological Features., Jonah Daneshmand

Electronic Theses and Dissertations

As single-cell RNA sequencing (scRNA-seq) data expands, robust methods for integrating diverse datasets are critical. This dissertation applies Persistent Homology (PH), a technique from Topological Data Analysis (TDA), to a collection of scRNA-seq datasets spanning eight tissue types to quantify how data integration affects topological features and biological interpretability. We assessed global topological structure using Betti curves, Euler characteristics, and persistence landscapes across raw, normalized, and integrated data representations. Our analysis revealed a performance inversion: while conventional methods excelled on unintegrated data, high-granularity topological methods, particularly those sensitive to global data structure, became superior after integration. This suggests a synergy …

Correlation With Car Density In Urban Environments And Its Influence On Chronic Obstructive Pulmonary Disease (Copd) Rates In The United States, Daniel Barreiro-Torres, Kedai Cheng

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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 …

Heuristic Weight Initialization For Transfer Learning In Classification Problems, Musulmon Lolaev, Anand Paul, Jeonghong Kim

School of Public Health Faculty Publications

Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully …

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 …

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 …

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 …

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 …

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), …

Differences In Covid-19 Deaths Amongst Cancer Patients And Possible Mediators For This Relationship, Leah Vaidya, Nubaira Rizvi, Xiao Cheng Wu, Lauren S. Maniscalco, Yong Yi, Augusto Ochoa, Qingzhao Yu

School of Public Health Faculty Publications

Previous research demonstrated Non-Hispanic Black populations experience higher COVID-19 mortality rates than Non-Hispanic White individuals. Additionally, cancer status is a known risk factor for COVID-19 death. While prior studies investigated comorbidities as exploratory variables in differences in COVID-19 hospitalization, none have explored their role in COVID-19-related deaths. This study aimed to evaluate whether Charlson Comorbidity Index (CCI) and subsequently, individual diseases are potential explanatory variables for this relationship. The analysis focused on Non-Hispanic Black and Non-Hispanic White cancer patients aged 20 or older, diagnosed between 2011 and 2019, who tested positive for COVID-19 from the start of pandemic through June …

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 …

Linking Water Quality And Climate Change To Long-Term Trends In Species Abundance In Norwalk Harbor, Viktoria Savatorova, Aidan Kieft, Nicole C. Spiller, Kasey Burns

Spora: A Journal of Biomathematics

This study examines the effects of environmental changes on fish populations in Norwalk Harbor, focusing on winter flounder (Pseudopleuronectes americanus), cunner (Tautogolabrus adspersus), northern pipefish (Syngnathus fuscus), and naked goby (Gobiosoma bosci) as examples of species responding to climate-related shifts. We analyze how water temperature, salinity, and dissolved oxygen correlate with fish abundance. To assess statistically significant differences in catch per unit effort (CPUE) across harbor regions, we applied the Kruskal-Wallis test followed by Dunn's post-hoc test. Seasonal variations in CPUE were examined by comparing monthly catch data for each species. K-means …

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 …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem

Dissertations, Master's Theses and Master's Reports

Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …

Cluster Effect For Snp-Snp Interaction Pairs For Predicting Complex Traits, Hui Yi Lin, Harun Mazumder, Indrani Sarkar, Po Yu Huang, Rosalind A. Eeles, Zsofia Kote-Jarai, Kenneth R. Muir, Johanna Schleutker, Nora Pashayan, Jyotsna Batra, David E. Neal, Sune F. Nielsen, Børge G. Nordestgaard, Henrik Grönberg, Fredrik Wiklund, Robert J. Macinnis, Christopher A. Haiman, Ruth C. Travis, Janet L. Stanford, Adam S. Kibel, Cezary Cybulski, Kay Tee Khaw, Christiane Maier, Stephen N. Thibodeau, Manuel R. Teixeira, Lisa Cannon-Albright, Hermann Brenner, Radka Kaneva, Hardev Pandha, Et Al

School of Public Health Faculty Publications

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a …

Disparities And Protective Factors In Pandemic-Related Mental Health Outcomes: A Louisiana-Based Study, Ariane L. Rung, Evrim Oral, Tyler Prusisz, Edward S. Peters

School of Public Health Faculty Publications

Introduction: The COVID-19 pandemic has had a wide-ranging impact on mental health. Diverse populations experienced the pandemic differently, highlighting pre-existing inequalities and creating new challenges in recovery. Understanding the effects across diverse populations and identifying protective factors is crucial for guiding future pandemic preparedness. The objectives of this study were to (1) describe the specific COVID-19-related impacts associated with general well-being, (2) identify protective factors associated with better mental health outcomes, and (3) assess racial disparities in pandemic impact and protective factors. Methods: A cross-sectional survey of Louisiana residents was conducted in summer 2020, yielding a sample of 986 Black …

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …

Examining The Interaction Between Calcium Supplement Use, Demographics, And Lifestyle Factors On Bone Health In Women, Vix Talbot

University Honors Theses

Osteoporosis is a condition which poses a significant health threat, particularly among women during the menopause transition, where accelerated bone loss increases fracture risk. Calcium supplementation has been shown to be an important intervention to mitigate bone mineral density (BMD) decline during this and other periods of life. However, the efficacy of calcium supplementation is influenced by various individual factors, including demographics and lifestyle habits. This study investigates the interaction between calcium supplement use, and several interaction terms on bone health in women. Multiple linear regression analysis is employed to assess the impact of these factors on BMD. Data from …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Deep Learning For Microbiome-Based Integrative Modeling And Microbial Biomarkers Identification, Sen Yang

Statistical Science Theses and Dissertations

The human microbiome, comprising trillions of microorganisms, plays a pivotal role in modulating host physiology via molecular and metabolite exchanges. One of the major challenges in this field lies in the effective integration of microbiome and metabolomics data, an achievement that holds the promise of substantially enhancing the precision of disease prediction. However, many datasets prioritize microbiome data while neglecting paired metabolome information. Additionally, the prevalent analytical tools face challenges in effectively merging these intricate datasets, leading to possible misinterpretations and reduced prediction accuracies.

To address these challenges, the first part of this research introduces the Microbiome-based Supervised Contrastive Learning …