Vital and Health Statistics Commons^™

Explainable Ai For Liver Transplant Survival Prediction: Integrating Immunological Mismatch Features, Sourab Shaik

Honors Projects

Liver Transplantations are crucial treatment for end-stage liver disease. However, a persistent deficit of donor organs necessitates maximizing the utility of each available graft to minimize failure rates. We evaluated whether donor–recipient molecular immunogenicity metrics - Electrostatic and Hydrophobic Mismatch Scores (HMS/EMS) and eplet-based counts - improve post–liver-transplant survival prediction. The analytic cohort comprised adult, first time, single-organ deceased-donor transplants drawn from Scientific Registry of Transplant Recipients; follow-up was truncated at five years, and the endpoint was all-cause graft failure (earliest of graft failure or death; otherwise, censored). HLA variables were derived via high- resolution conversion and molecular mismatch computations …

Association Of Estimated Plasma Volume Status With Invasive Hemodynamics And Adverse Clinical Outcomes In Patients With Pulmonary Hypertension And Chronic Kidney Disease, Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara John, Rasha Khan, Ian Mclaren, Alexander Prendergast, Simone Jarrett, Kevin Bryan Lo, Christian Witzke

Einstein Health Papers

Identifying noninvasive measures to assess intravascular volume status and risk stratify patients with pulmonary hypertension (PH) and chronic kidney disease (CKD) is needed. We assessed the predictive value of estimated plasma volume status (ePVS) using the Strauss-derived Duarte formula in PH-CKD patients. This single-center retrospective cohort analysis included patients with PH and CKD Stage 3b (CKD3b), Stage 4 (CKD4), or Stage 5 (CKD5) who underwent right heart catheterization from 2018 to 2023. Patients were categorized into low ePVS (< 6.2) and high ePVS (≥ 6.2) using Youden's J statistics. We used the Cox-proportional hazards model, adjusting for age, sex, and body mass index, to investigate the association between …

Breast Cancer Survival Rates And Determinants In Ethiopia: A Systematic Review And Meta-Analysis Of Longitudinal Studies, Abenezer M. Tafese, Meseker T. Fentie, Beminate L. Seifu, Angwach A. Asnake, Bikiltu D. Dirbaba, Abdisa G. Jara, Elsabeth Tizazu Asare, Brandon George

College of Population Health Faculty Papers

BACKGROUND: Breast cancer is the most common cancer and the leading cause of cancer mortality among women in Ethiopia, accounting for 32% of new cancer cases and 17.6% of cancer deaths. Despite its growing burden, comprehensive data on survival rates and contributing factors remain limited. This systematic review and meta-analysis aimed to synthesize existing data on breast cancer survival in Ethiopia and identify key determinants influencing outcomes.

METHODS: A comprehensive systematic search was conducted in PubMed, Web of Science, Scopus, Embase, and CINAHL to identify studies on breast cancer survival in Ethiopia published between January 2014 and August 2024. Eligible …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

Analysis Of Errors In The Management Of Cutaneous Disorders, Robert J. Pariser, Sarah Alnaif

Department Dermatology Faculty Publications

In this study, we prospectively and retrospectively evaluated the occurrence of errors in the management of cutaneous disorders from patient visits and medical records in a single dermatology practice in southeast Virginia over a 3-year period (June 2020-July 2023). Providers should be able to improve diagnostic accuracy by utilizing established rapid bedside diagnostic techniques.

Pediatric Cancer Incidence, Temporal Trends, And Mortality In The United States By Health Disparities Indicators, Seer (1973-2014), Prachi P. Chavan, Laurens Holmes Jr.

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pediatric cancer incidence has been increasing in the United States, despite improvement in pediatric cancer survival. This steady increase in incidence trends is not completely understood but maybe associated with social and environmental factors. In this study we aimed to assess the cumulative incidence, temporal trends, and mortality rates in pediatric cancer. Additionally, we examined sub-group variability in both incidence and mortality rates. Methods: Data from Surveillance, Epidemiology, and End Results (SEER) −18 from 1973–2014 were used for the purpose of analysis in this study. Age-adjusted incidence rates were used to assess temporal trends in cancer among children aged < 1–19 years. Univariable and multivariable binomial regression models were used to examine the association between race and cancer mortality while adjusting for potential confounders. Results: There were 92,594 cancer diagnoses during this period. White children comprised 74,758, (80.7%), black children 10,030, (10.8%), and other races 6648, (7.2%). Overall the age-adjusted cumulative incidence was slightly higher among white children (16.4%) than black children (12.4%) and other (13.0%). Children aged 15–19 years and those in metropolitan regions were more likely to be diagnosed with pediatric cancer. Relative to females, males were 16% more likely to die from the disease [adjusted Risk Ratio (aRR): 1.16, 95% Confidence Interval (CI): 1.09–1.22]. Additionally, compared to white children, black children had higher mortality rates [(aRR): 1.37, 99% CI: 1.23–1.52]. Conclusions: There is an increasing trend in pediatric cancer incidence; while white children have the highest incidence, black children and males indicated a survival disadvantage, indicative of racial and sex variability in overall pediatric cancer in the United States.

Effect Of Universal Health Coverage On Neonatal Mortality Rate In Sub-Saharan Africa: Addressing Missing Data In Health Indicators, Elizabeth Nalule Arihoona

Graduate Research Theses & Dissertations

This study utilizes two mixed-effects models to examine the effect of Universal Health Coverage on Neonatal Mortality Rate (NMR) in 30 countries of Sub-Saharan Africa from 2000-2019 while addressing the missing data in health indicators. Using natural cubic spline interpolation, I impute the missing values in the health coverage indices to preserve the location- specific trends of the data. Findings indicate that among the health Coverage indices, only Index1, which directly relates to coverage of maternal, newborn and child health services is significantly associated with reduced NMR. While higher Current Health Expenditure is associated with reduced NMR, a higher share …

Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi

Theses and Dissertations

Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …