Medicine and Health Sciences Commons^™

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Enhancing Public Health Surveillance: Development And Validation Of Machine Learning Models For Suspected Opioid Overdose Detection In Emergency Medical Services Data, Peter J. Rock

Theses and Dissertations--Clinical and Translational Science

The ongoing opioid overdose crisis in the United States requires timely and accurate surveillance systems to inform public health responses. Traditional public health surveillance methods rely on hospital discharge data and death certificates, which suffer from significant reporting delays and miss cases where patients refuse hospital transportation. Emergency Medical Services (EMS) data presents a promising alternative with advantages in timeliness and case ascertainment but lacks validated definitions for suspected opioid overdose (SOO).

This dissertation addresses this critical gap through the development, validation, and fairness assessment of machine learning models with natural language processing (ML-NLP) for identifying SOOs in EMS data. …

Mechanistic Insights Into The Antimicrobial Action Of Fungus-Derived Xanthoquinodins Against Clostridioides Difficile, Erika A. Serravalle

Graduate Thesis and Dissertation post-2024

The bacterial pathogen Clostridioides difficile is the leading cause of antibiotic- and healthcare-associated diarrhea, imposing significant clinical and economic impacts. The emergence of hypervirulent strains, such as PCR ribotype 027 (RT027), has exacerbated disease severity, recurrence rates, antibiotic resistance, and epidemiological spread. Expanding resistance profiles among C. difficile strains continue to limit the effectiveness of current IDSA-SHEA recommended therapies, which are already compromised by high recurrence rates due to antibiotic-induced disruption of the gut microbiota. These challenges highlight the urgent clinical need to identify and characterize antimicrobial compounds targeting alternative pathways in C. difficile physiology. We report the discovery and …

Synthesis And Evaluation Of Chalcones As Treatment For Negative Sense Single Stranded Rna Viruses And Applications As Broad-Spectrum Antivirals, Lorael Kirton

Graduate Thesis and Dissertation post-2024

Chalcones are a class of compounds naturally found in plants and are predicated upon a 1,3-diphenylprop-2-en-1-one scaffold. Chalcones possess many biological activities ranging from anticancer to antiviral activities. This thesis investigates the broad-spectrum antiviral properties of chalcones. Some chalcones have been reported to affect host cell mechanisms such as mammalian target of rapamycin (mTOR) signaling cascade or the cell cycle to reduce viral replication. Previously, a series of chalcones 8 were synthesized and investigated in human cytomegalovirus virus (HCMV) and human immunodeficiency virus (HIV). In this study, ten new chalcones predicated upon the design of 8 were synthesized and a …

Fgf19 Facilitates Metastatic Phenotypes In Colorectal Cancer, Joseph W. Nolff

Graduate Thesis and Dissertation post-2024

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. With 154,000 new cases estimated in 2025, there is an increased need for early screening to detect CRC prior to metastasis. Our laboratory has focused on enteroendocrine hormone Fibroblast Growth Factor-19 (FGF19) as it is overexpressed in a subset of CRC tumors. FGF19 is an intestinal-derived hormone involved with lipid homeostasis when bound to its receptor, Fibroblast Growth Factor Receptor-4 (FGFR4). Our objective focuses on FGF19 contribution towards an epithelial-to-mesenchymal transition (EMT) and overall metastatic properties including rapid proliferation and/or increased cell motility. Immunofluorescence was …

Symbol-Temporal Consistency Self-Supervised Learning For Robust Time Series Classification, Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao

Computer Science Faculty Publications

The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity …

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

Leveraging Snomed Ct For Patient Cohort Identification Over Heterogeneous Ehr Data, Xubing Hao, Yan Huang, Licong Cui, Xiaojin Li

Faculty, Staff and Student Publications

SNOMED CT is extensively employed to standardize data across diverse patient datasets and support cohort identification, with studies revealing its benefits and challenges. In this work, we developed a SNOMED CT-driven cohort query system over a heterogeneous Optum® de-identified COVID-19 Electronic Health Record dataset leveraging concept mappings between ICD-9-CM/ICD-10-CM and SNOMED CT. We evaluated the benefits and challenges of using SNOMED CT to perform cohort queries based on both query code sets and actual patients retrieved from the database, leveraging the original ICD-9-CM and ICD-10-CM as baselines. Manual review of 80 random cases revealed 65 cases containing 148 true positive …

Leveraging Gpt-4o For Automated Extraction Of Neural Projections From Scientific Literature, Rashmie Abeysinghe, Gorbachev Jowah, Licong Cui, Samden D Lhatoo, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to …

Identifying Acute Myeloid Leukemia Subtypes Based On Pathway Enrichment, Ling Zhong, Jiangti Luo, Junze Dong, Xiang Yang, Xiaosheng Wang

Faculty, Staff and Student Publications

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults and the second most common in children. Despite the introduction of targeted therapies, AML survival rates have shown limited improvement, particularly among older patients. This study explored personalized treatment strategies for AML by proposing a novel subtyping method. Through unsupervised clustering based on the enrichment scores of 14 pathways related to metabolism, immunity, DNA repair, and oncogenic signaling, we identified three AML subtypes: DNA repair (DR), immune-enriched (ImE), and immune-deprived (ImD), consistent in four independent datasets. DR is marked by high expression of DNA repair and …

Gene Expression Changes In Human Cerebral Arteries Following Hemoglobin Exposure: Implications For Vascular Responses In Sah, Chathathayil M Shafeeque, Arif O Harmanci, Sithara Thomas, Ari C Dienel, Devin W Mcbride, Kumar T Peeyush, Spiros L Blackburn

Faculty, Staff and Student Publications

Subarachnoid hemorrhage (SAH), characterized by the presence of hemoglobin (Hb) in the subarachnoid space, significantly impacts cerebral vessels, leading to various pathological outcomes. The toxicity of cell-free Hb released from erythrocytes and its metabolites after SAH causes vasoconstriction and neuronal damage, and correlates with delayed ischemic neurological deficits (DIND). While animal models have provided substantial and invaluable data in the research of aneurysmal SAH, the specific effects of subarachnoid blood on cerebral arteries remain greatly understudied. Here, we describe the changes in the genetic profile of human cerebral arteries exposed to free Hb for 48 h. We performed an ex …

Automatic Summarization Of Doctor-Patient Encounter Dialogues Using Large Language Model Through Prompt Tuning, Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian, Yonghui Wu

Faculty, Staff and Student Publications

Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark …

Designing A Narrative-Based Video Game For Adolescents Coping With A Parent's Cancer, Sophia Hamilton, Xubing Hao, Sharon Andrews, Eveyln Roldan, Carlos Martinez, Jane Hamilton, Muhammad Tuan Amith, Licong Cui

Faculty, Staff and Student Publications

We developed the narrative video game Sophia that immerses users in the story of a 13-year-old girl during her father's 18-month battle with glioblastoma and her journey toward resilience during her high school years following his passing. The game is designed to engage adolescents coping with parental cancer in multiple gameplay modes and levels, the character, and the outcome of the story as a way to build connections and explore the themes of illness, dying, bereavement, and resilience. The scenes at each level correspond to the stages of glioblastoma illness as well as the teenage milestones she passes through without …

Neurotensin Regulates Primate Ovulation Via Multiple Neurotensin Receptors, Andrew C. Pearson, Jessica S. Miller, Hannah J. Jensen, Ketan Shrestha, Thomas E. Curry Jr., Diane M. Duffy

Department of Biomedical and Translational Sciences Faculty Publications

Neurotensin (NTS), a small neuropeptide, was recently established as a key paracrine mediator of ovulation. NTS mRNA is highly expressed by granulosa cells in response to the luteinizing hormone (LH) surge, and multiple NTS receptors are expressed by cells of the ovulatory follicle. To identify the role of NTS receptors NTSR1 and SORT1 in ovulation in vivo, the dominant follicle of cynomolgus macaques (Macaca fascicularis) was injected with either vehicle control, the general NTS receptor antagonist SR142948, the NTSR1-selective antagonist SR48692, or the SORT1-selective antagonist AF38469. Human chorionic gonadotropin (hCG) was then administered to initiate ovulatory events. …

Depletion Of Adipose Stroma-Like Cancer-Associated Fibroblasts Potentiates Pancreatic Cancer Immunotherapy, Joseph Rupert, Alexes Daquinag, Yongmei Yu, Yulin Dai, Zhongming Zhao, Mikhail G Kolonin

Faculty, Staff and Student Publications

This study shows that populations of CAFs have distinct effects on pancreatic cancer progression and shows that depletion of CAFs expressing adipose markers potentiates tumor/metastasis suppression effects of immune checkpoint blockade.

Tagraxofusp Maintenance Post-Hematopoietic Stem Cell Transplantation Provides Long-Term Survival And Manageable Safety For A Patient With Blastic Plasmacytoid Dendritic Cell Neoplasm, Qaiser Bashir, Marina Konopleva, Glorette Abueg, Jeremy Ramdial, Chitra Hosing, Samer A Srour, Amin Alousi, Uday R Popat, Yago Nieto, Gheath Alatrash, Richard E Champlin, Elizabeth J Shpall, Muzaffar Qazilbash, Naveen Pemmaraju

Faculty, Staff and Student Publications

Presented here is the case of a 68-year-old woman with blastic plasmacytoid dendritic cell neoplasm (BPDCN) treated with tagraxofusp (TAG) maintenance therapy post-allogeneic hematopoietic stem cell transplantation (allo-HCT). Prior to allo-HCT, the patient was treated with hydroxyurea and mini-CVD (cyclophosphamide, vincristine, and dexamethasone alternating with methotrexate (Methotrexate) and cytarabine) + venetoclax + TAG for 5 cycles, which induced morphologic complete remission with minimal residual disease. After allo-HCT, the patient had persistent cytogenic abnormalities 45,XX,der(7)add(7)(p13)del(7)(q11.2q22)add(7)(q32),add(12)(p13),-15,del(16)(q23),-17,+22,+2mar[1]/46,XX[19], and was then treated with TAG maintenance therapy at 9 mg/kg on a 28-day cycle for 16 cycles. At mid-treatment (cycle 6 of 16 cycles of …

Co-Morbid Indomethacin-Responsive Headaches In A Woman In Her Late 60s With Paroxysmal Hemicrania And Hypnic Headache: A Case Report, Ashlyn Brown, Randolph W Evans, Claudia Carrizo, Mark Burish

Faculty, Staff and Student Publications

Paroxysmal hemicrania (PH) and hypnic headache (HH) are rare indomethacin-responsive headache syndromes. This case report details the new onset of both disorders in a woman in her late 60s. One headache type presented as severe pain centered on the right eyebrow, lasting 30 minutes, occurring more than 8 times daily, and associated with ipsilateral lacrimation and rhinorrhea. The second type was a right frontal severe pain, with onset at 4 a.m., occurring only during sleep, lasting 30 minutes, and with no associated factors. The patient's response to indomethacin for both headache types was confirmed through an unblinded ABAB study design: …

A Bayesian Deep Segmentation Framework For Glioblastoma Tumor Segmentation Using Follow-Up Mris, Tanjida Kabir, Kang-Lin Hsieh, Luis Nunez, Yu-Chun Hsu, Juan C Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jay-Jiguang Zhu, Roy F Riascos, Mahboubeh Madadi, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this …

Examining Educational And Career Transition Points Among A Diverse, Virtual Mentoring Network, Erika L Thompson, Toufeeq Ahmed Syed, Zainab Latif, Katie Stinson, Damaris Javier, Gabrielle Saleh, Jamboor K Vishwanatha

Faculty, Staff and Student Publications

Given the differences in trajectory for under-represented minorities in biomedical careers, we sought to explore how a virtual mentoring program, the National Research Mentoring Network (NRMN), and its platform (MyNRMN), may facilitate transitions in the science, technology, engineering, mathematics, and medicine (STEMM) pipeline. The purpose of this study was to describe how the size of an MyNRMN member’s mentoring network and level of engagement correlate with academic and career transitions. We examined MyNRMN platform user data from March 2020 to May 2021 (n = 2993). Logistic regression estimated the odds of a career or academic transition related to NRMN …

Key Considerations For Combination Therapy In Alzheimer’S Clinical Trials: Perspectives From An Expert Advisory Board Convened By The Alzheimer’S Drug Discovery Foundation, Jeffrey Cummings, Michael Gold, Mark Mintun, Michael Irizarry, Andrew Von Eschenbach, Suzanne Hendrix, Donald Berry, Cristina Sampaio, Kaycee Sink, Jaren Landen, Miia Kivipelto, Michael Grundman, Steven E Arnold, Allan Green, Katherine Partrick, Laura Nisenbaum, Aaron Burstein, Howard Fillit

Faculty, Staff and Student Publications

There is growing consensus in the Alzheimer's community that combination therapy will be needed to maximize therapeutic benefits through the course of the disease. However, combination therapy raises complex questions and decisions for study sponsors, from preclinical research through clinical trial design to regulatory, statistical, and operational considerations. In January 2024, the Alzheimer's Drug Discovery Foundation convened an expert advisory board to discuss the key considerations in each of these areas. Experts agreed on the need to prioritize a combination therapy approach that encompasses a wide range of targets associated with aging and the underlying biology of Alzheimer's disease. Progress …

Assessing Impact Of Molecular Diagnostics On Time To Optimal Antibiotic Therapy In Pediatric Staphylococcal Bacteremia, Manith Humchad, Kelly Williamson

Williams Honors College, Honors Research Projects

Timely optimization of antibiotic therapy is critical in pediatric patients with blood infections, particularly those caused by Staphylococcus aureus. This retrospective study evaluated the impact of molecular diagnostics on time to optimal therapy and discontinuation of unnecessary antibiotics at Akron Children’s Hospital from January 2020 to April 2023. Patients with Methicillin-resistant Staphylococcus aureus (MRSA) were more likely to be on appropriate empiric therapy (i.e. vancomycin) before culture results, whereas Methicillin-sensitive Staphylococcus aureus (MSSA) cases were de-escalated to beta-lactams. The median time to optimal therapy was 3.7 hours for episodes of MSSA. Infectious Disease (ID) consultation significantly influenced antibiotic management, with …

A Serum Biomarker Panel And Miniarray Detection System For Tracking Disease Activity And Flare Risk In Lupus Nephritis, Chenling Tang, Gongjun Tan, Aygun Teymur, Jiechang Guo, Arturo Haces-Garcia, Weihang Zhu, Richard Williams, Jing Ning, Ramesh Saxena, Tianfu Wu

Faculty, Staff and Student Publications

Introduction: Lupus nephritis (LN) leads to end stage renal disease (ESRD), and early diagnosis and disease monitoring of LN could significantly reduce the risk. however, there is not such a system clinically. In this study we aim to develop a biomarker-panel based point-of-care system for LN.

Methods: Immunoassay screening combined with genomic expression databases and machine learning techniques was used to identify a biomarker panel of LN. A quantitative biomarker-panel mini-array (BPMA) system was developed and the sensitivity, specificity, reproducibility, and stability of the were examined. The performance of BPMA in disease monitoring was validated with machine models using a …

Effect Of Digital Health Coaching On Self-Efficacy And Patient-Reported Outcomes In Individuals With Acute Myeloid And Chronic Lymphocytic Leukemia: A Pilot Randomized Controlled Trial, Jennifer Marvin-Peek, Valerie Shelton, Kelly Brassil, Bryan Fellman, Austin Barr, Kelly Sharon Chien, Danielle Hammond, Mahesh Swaminathan, Nitin Jain, William Wierda, Alessandra Ferrajoli, Courtney Dinardo

Faculty, Staff and Student Publications

Introduction: Promotion of self-efficacy can enhance engagement with health care and treatment adherence in patients with cancer. We report the outcomes of a pilot trial of a digital health coach intervention in patients with leukemia with the aim of improving self-efficacy.

Methods: Adult patients with newly diagnosed acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) were randomized 1:1 to a digital health coach intervention or standard of care. The primary outcome of self-efficacy was measured by the Cancer Behavior Inventory (CBI) score.

Results: A total of 147 patients (37 AML, 110 CLL) were enrolled from July 2020 to December …

Enhancing Clinical Trial Matching In Molecular Diagnostics: Using Natural Language Processing And Clustering Approaches In Hematological Malignancies, Gillian Fanning

Theses and Dissertations

Clinical trial matching is a critical component of personalized medicine, particularly in the management of hematologic malignancies. At Virginia Commonwealth University (VCU) Health, the Molecular Diagnostics (MDX) Lab produces somatic variant reports and recommends clinical trials based on the presence of clinically significant mutations. However, the current manual trial recommendation process is time-intensive and lacks scalability.

This study introduces a computational framework to streamline and standardize clinical trial matching using natural language processing (NLP) and unsupervised clustering. Trial brief descriptions were analyzed to extract frequent terms, and trials were grouped based on term similarity using joint dimensionality reduction and clustering. …

Rethinking Parkinson's Disease Genetics In The Precision Medicine Era: Why Genomic Diversity Matters?, Camilla Teixeira Pinheiro Gusmão, Giselli Scaini, Everton Ferreira De Souza, Rafael Antônio Vicente Lacerda, Matheus De Almeida Costa, Raja Mehanna, João Quevedo, Howard Lopes Ribeiro Junior

Faculty, Staff and Student Publications

No abstract provided.

Topss: Tolerability Of Transcranial Direct Current Stimulation In Pediatric Stroke Survivors, Stuart Fraser, Anna Clearman, Melika Abrahams, Bernadette Gillick, Tia Lal, Sean Savitz, Nuray Yozbatiran

Faculty, Staff and Student Publications

Background: Transcranial direct current stimulation is a non-invasive neuromodulation technique with emerging therapeutic potential in neurodevelopmental conditions. While childhood-onset stroke survivors frequently experience long-term motor impairment, there are very few studies examining the safety and feasibility of transcranial direct current stimulation in this population.

Objective: To evaluate the safety, feasibility, and tolerability of bihemispheric transcranial direct current stimulation paired with occupational therapy in children and adolescents with chronic hemiparesis following childhood-onset arterial ischemic stroke or intracranial hemorrhage.

Methods: In this single-arm, open-label pilot study, five participants aged 6-19 years of age received five daily sessions of transcranial direct current stimulation …

Smoking Habit And Long-Term Colorectal Cancer Incidence By Exome-Wide Mutational And Neoantigen Loads: Evidence Based On The Prospective Cohort Incident-Tumour Biobank Method, Tsuyoshi Hamada, Tomotaka Ugai, Carino Gurjao, Satoko Ugai, Xuehong Zhang, Koichiro Haruki, Yasutoshi Takashima, Naohiko Akimoto, Mai Chan Lau, Kosuke Matsuda, Nobuhiro Nakazawa, Mayu Higashioka, Satoshi Miyahara, Keisuke Kosumi, Yohei Masugi, Li Liu, Yin Cao, Daniel Nevo, Molin Wang, Reiko Nishihara, Sachet A Shukla, Catherine J Wu, Levi A Garraway, Jeffrey A Meyerhardt, Edward L Giovannucci, Jonathan A Nowak, Charles S Fuchs, Andrew T Chan, Mingyang Song, Marios Giannakis, Shuji Ogino

Faculty, Staff and Student Publications

Objective: To test the hypothesis that the association of smoking with long-term colorectal cancer incidence may be stronger for tumours with higher mutational and neoantigen loads.

Methods and analysis: In the Nurses' Health Study (1980-2012) and the Health Professionals Follow-up Study (1986-2012), our novel prospective cohort incident-tumour biobank method (PCIBM) used 3053 incident colorectal carcinoma cases including 752 cases with whole-exome sequencing data. Using the multivariable duplication-method Cox regression model with the inverse probability weighting to adjust for the selection bias due to tissue availability, we assessed a differential association of cigarette smoking with colorectal carcinoma incidence by an exome-wide …

Reusable Generic Clinical Decision Support System Module For Immunization Recommendations In Resource-Constraint Settings, Samuil Orlioglu, Akash Shanmugan Boobalan, Kojo Abanyie, Richard D Boyce, Hua Min, Yang Gong, Dean F Sittig, Paul Biondich, Adam Wright, Christian Nøhr, Timothy Law, David Robinson, Arild Faxvaag, Nina Hubig, Ronald Gimbel, Lior Rennert, Xia Jing

Faculty, Staff and Student Publications

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules. Our solution includes a common CDSS module, electronic medical record (EMR) specific adapters, CDSS rules written in the clinical quality language (CQL) (derived from CDC immunization recommendations), and patient records in fast healthcare interoperability resources (FHIR) format. The proposed CDSS is entirely browser-based and reachable within the user's EMR interface at the client-side. This helps to avoid …

Exploring The Inequitable Impact Of Data Missingness On Fairness In Machine Learning, Sitao Min, Hafiz Asif, Jaideep Vaidya

Faculty, Staff and Student Publications

Today, data-driven models and artificial intelligence / machine learning underlie decision making in almost all aspects of society. However, significant concerns have been raised over the fairness of such models. While various aspects of algorithmic fairness have been studied, the effect of missing data on fairness remains understudied. This is a significant problem since data in real-world settings is almost never complete, and may often suffer from systemic missingness. This article systematically evaluates how missing data, particularly when correlated with protected classes and outcome variables, affects the fairness of classifiers. Utilizing a comprehensive framework covering various missing data patterns, rates, …

Disposition Outcomes Following Prehospital Use Of Naloxone In A Large Metropolitan City In The United States, James R Langabeer, Christine Bakos-Block, A Sarah Cohen, Ishmam Alam, Bhanumathi Gopal, Marylou Cardenas-Turanzas, Arlo F Weltge, David Persse, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Objectives: During a drug overdose, research suggests individuals may not call 9-1-1 out of fear of criminal justice concerns. Of those that call, research is inconclusive about the disposition of the emergency transport. We evaluated transport outcomes for adults with opioid-related overdose in the Emergency Medical Services (EMS) of a large metropolitan city in the United States.

Methods: We reviewed the EMS incident report database from the patient care record system for years 2018 to 2022. We queried all records, searching for relevant terms, and two reviewers cross-checked the database to identify cases that did not result in death at …