Data Science Commons^™

Modeling Of Cns Cancer With A Focus On The Immune Component, Daniel Zamler

Dissertations and Theses (Open Access)

The knowledge surrounding cancers of the central nervous system remains poorly developed, in particular with regard to the immune component. The works contained in this thesis look at craniopharyngioma, glioblastoma, and several forms of brain metastasis. While some attention is given to the tumor cells themselves, as well as the patient setting which these studies model, the immune component of disease progression and treatment plays a strong role in each and is the primary focus of the works contained.

Craniopharyngioma is a relatively rare tumor in adults. Although histologically benign, it can be locally aggressive and may require additional therapeutic …

Time Dependent Analysis Of Rat Microglial Surface Markers In Traumatic Brain Injury Reveals Dynamics Of Distinct Cell Subpopulations, Assaf Gottlieb, Naama Toledano-Furman, Karthik S Prabhakara, Akshita Kumar, Henry W Caplan, Supinder Bedi, Charles S Cox, Scott D Olson

Faculty, Staff and Student Publications

Traumatic brain injury (TBI) results in a cascade of cellular responses, which produce neuroinflammation, partly due to the activation of microglia. Accurate identification of microglial populations is key to understanding therapeutic approaches that modify microglial responses to TBI and improve long-term outcome measures. Notably, previous studies often utilized an outdated convention to describe microglial phenotypes. We conducted a temporal analysis of the response to controlled cortical impact (CCI) in rat microglia between ipsilateral and contralateral hemispheres across seven time points, identified microglia through expression of activation markers including CD45, CD11b/c, and p2y12 receptor and evaluated their activation state using additional …

The Impact Of Pediatric Opioid-Related Visits On Us Emergency Departments, Tiffany Champagne-Langabeer, Marylou Cardenas-Turanzas, Irma T Ugalde, Christine Bakos-Block, Angela L Stotts, Lisa Cleveland, Steven Shoptaw, James R Langabeer

Faculty, Staff and Student Publications

BACKGROUND: While there is significant research exploring adults' use of opioids, there has been minimal focus on the opioid impact within emergency departments for the pediatric population.

METHODS: We examined data from the Agency for Healthcare Research, the National Emergency Department Sample (NEDS), and death data from the Centers for Disease Control and Prevention. Sociodemographic and financial variables were analyzed for encounters during 2014-2017 for patients under age 18, matching diagnoses codes for opioid-related overdose or opioid use disorder.

RESULTS: During this period, 59,658 children presented to an ED for any diagnoses involving opioids. The majority (68.5%) of visits were …

Privacy-Preserving Logistic Regression With Secret Sharing, Ali Reza Ghavamipour, Fatih Turkmen, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.

METHODS: In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton-Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority …

Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Pora Kim, Hua Tan, Jiajia Liu, Himansu Kumar, Xiaobo Zhou

Faculty, Staff and Student Publications

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

The Clock Modulator Nobiletin Mitigates Astrogliosis-Associated Neuroinflammation And Disease Hallmarks In An Alzheimer’S Disease Model, Marvin Wirianto, Chih-Yen Wang, Eunju Kim, Nobuya Koike, Ruben Gomez-Gutierrez, Kazunari Nohara, Gabriel Escobedo, Jong Min Choi, Chorong Han, Kazuhiro Yagita, Sung Yun Jung, Claudio Soto, Hyun Kyoung Lee, Rodrigo Morales, Seung-Hee Yoo, Zheng Chen

Faculty, Staff and Student Publications

Alzheimer's disease (AD) is a devastating neurodegenerative disorder, and there is a pressing need to identify disease-modifying factors and devise interventional strategies. The circadian clock, our intrinsic biological timer, orchestrates various cellular and physiological processes including gene expression, sleep, and neuroinflammation; conversely, circadian dysfunctions are closely associated with and/or contribute to AD hallmarks. We previously reported that the natural compound Nobiletin (NOB) is a clock-enhancing modulator that promotes physiological health and healthy aging. In the current study, we treated the double transgenic AD model mice, APP/PS1, with NOB-containing diets. NOB significantly alleviated β-amyloid burden in both the hippocampus and the …

Counterfactual Analysis Of Differential Comorbidity Risk Factors In Alzheimer’S Disease And Related Dementias, Yejin Kim, Kai Zhang, Sean I Savitz, Luyao Chen, Paul E Schulz, Xiaoqian Jiang

Faculty, Staff and Student Publications

Alzheimer’s disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with various comorbidities. There is also significant heterogeneity in the prevalence of ADRD across diverse demographics groups. Association studies on such heterogeneous comorbidity risk factors are limited in their ability to determine causation. We aim to compare counterfactual treatment effects of various comorbidity in ADRD in different racial groups (African Americans and Caucasians). We used 138,026 ADRD and 1:1 matched older adults without ADRD from nationwide electronic health records, which extensively cover a large population’s long medical history in breadth. We matched African Americans …

Use Of The Deep Learning Approach To Measure Alveolar Bone Level, Chun-Teh Lee, Tanjida Kabir, Jiman Nelson, Sally Sheng, Hsiu-Wan Meng, Thomas E Van Dyke, Muhammad F Walji, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

AIM: The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis.

MATERIALS AND METHODS: A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the …

Multiple Approaches Converge On Three Biological Subtypes Of Meningioma And Extract New Insights From Published Studies, James C Bayley, Caroline C Hadley, Arif O Harmanci, Akdes S Harmanci, Tiemo J Klisch, Akash J Patel

Faculty, Staff and Student Publications

One-fifth of meningiomas classified as benign by World Health Organization (WHO) histopathological grading will behave malignantly. To better diagnose these tumors, several groups turned to DNA methylation, whereas we combined RNA-sequencing (RNA-seq) and cytogenetics. Both approaches were more accurate than histopathology in identifying aggressive tumors, but whether they revealed similar tumor types was unclear. We therefore performed unbiased DNA methylation, RNA-seq, and cytogenetic profiling on 110 primary meningiomas WHO grade I and II). Each technique distinguished the same three groups (two benign and one malignant) as our previous molecular classification; integrating these methods into one classifier further improved accuracy. Computational …

Verticox: Vertically Distributed Cox Proportional Hazards Model Using The Alternating Direction Method Of Multipliers, Wenrui Dai, Xiaoqian Jiang, Luca Bonomi, Yong Li, Hongkai Xiong, Lucila Ohno-Machado

Faculty, Staff and Student Publications

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the …

Machine Learning For Predicting Risk Of Early Dropout In A Recovery Program For Opioid Use Disorder, Assaf Gottlieb, Andrea Yatsco, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

BACKGROUND: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.

METHODS: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning …

Sociodemographic And Clinical Characteristics Associated With Improvements In Quality Of Life For Participants With Opioid Use Disorder, Assaf Gottlieb, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant's reported quality of life (QoL) at the beginning of the program and at successive intervals.

Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL.

Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, …

Lncrnafunc: A Knowledgebase Of Lncrna Function In Human Cancer, Mengyuan Yang, Huifen Lu, Jiajia Liu, Sijia Wu, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse human diseases. To date, systematic and intensive annotations on diverse interaction regulations of lncRNAs in human cancer were not available. Here, we built lncRNAfunc, a knowledgebase of lncRNA function in human cancer at https://ccsm.uth.edu/lncRNAfunc, aiming to provide a resource and reference for providing therapeutically targetable lncRNAs and intensive interaction regulations. To do this, we collected 15 900 lncRNAs across 33 cancer types from TCGA. For individual lncRNAs, we performed multiple interaction analyses of different biomolecules including DNA, RNA, and protein levels. …

Fusiongdb 20: Fusion Gene Annotation Updates Aided By Deep Learning, Pora Kim, Hua Tan, Jiajia Liu, Haeseung Lee, Hyesoo Jung, Himanshu Kumar, Xiaobo Zhou

Faculty, Staff and Student Publications

A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human …

Automated Identification Of Missing Is-A Relations In The Human Phenotype Ontology, Maryamsadat Mohtashamian, Ran Hu, Rashmie Abeysinghe, Xubing Hao, Hua Xu, Licong Cui

Faculty, Staff and Student Publications

Auditing the Human Phenotype Ontology (HPO) is necessary to provide accurate terminology for its use in clinical research. We investigate an approach leveraging the lexical features of concepts in HPO to identify missing IS-A relations among HPO concepts. We first model the names of HPO concepts as sets of words in lower case. Then, we generate two types of concept-pairs which have at least a single common word: (1) Linked concept-pairs generated from concept-pairs having an IS-A relation; (2) Unlinked concept-pairs generated from concept-pairs without an IS- A relation. Concept-pairs generate Derived Term Pairs (DTPs) emphasizing unique lexical information of …

Cost Of Care For Asylum Seekers And Refugees Entering The United States: The Case Of Volunteer Medical Providers In El Paso, Texas, Rigoberto I Delgado, Manuel De La Rosa, Marlon A Picado, Lisa Ayoub-Rodriguez, Celia E Gonzalez, Leopold Gemoets

Faculty, Staff and Student Publications

BACKGROUND: Between October 2018, and February 2020, the United States saw an unprecedented increase in the number of asylum seekers and refugees arriving unexpectedly at international crossings along the US-Mexico Border. Many of these migrants needed proper medical attention, and consequently created significant pressure on local health systems. In El Paso, Texas, volunteer clinicians, collaborating closely with religious organizations and non-governmental organizations, provided outpatient medical care for the new arrivals; the county hospital provided in-patient care at local tax payers' expense. The objective of this study was to estimate costs of healthcare services offered by these volunteers in order to …

Dat3m: A Data Tracker For Multi-Faceted Management Of Multi-Site Clinical Research Data Submission, Curation, Master Inventorying, And Sharing, Shiqiang Tao, Licong Cui, Wei-Chun Chou, Samden Lhatoo, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, …

Temporal Cohort Logic, Guo-Qiang Zhang, Xiaojin Li, Yan Huang, Licong Cui

Faculty, Staff and Student Publications

We introduce a new logic, called Temporal Cohort Logic (TCL), for cohort specification and discovery in clinical and population health research. TCL is created to fill a conceptual gap in formalizing temporal reasoning in biomedicine, in a similar role that temporal logics play for computer science and its applications. We provide formal syntax and semantics for TCL and illustrate the various logical constructs using examples related to human health. Relationships and distinctions with existing temporal logical frameworks are discussed. Applications in electronic health record (EHR) and in neurophysiological data resource are provided. Our approach differs from existing temporal logics, in …

Causal Inference Of Genetic Variants And Genes In Amyotrophic Lateral Sclerosis, Siyu Pan, Xinxuan Liu, Tianzi Liu, Zhongming Zhao, Yulin Dai, Yin-Ying Wang, Peilin Jia, Fan Liu

Faculty, Staff and Student Publications

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as …

Deep Graph Convolutional Network For Us Birth Data Harmonization, Lishan Yu, Hamisu M Salihu, Deepa Dongarwar, Luyao Chen, Xiaoqian Jiang

Faculty, Staff and Student Publications

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more …

Facilitating Federated Genomic Data Analysis By Identifying Record Correlations While Ensuring Privacy, Leonard Dervishi, Xinyue Wang, Wentao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record …

Caries Risk Documentation And Prevention: Emeasures For Dental Electronic Health Records, Suhasini Bangar, Ana Neumann, Joel M White, Alfa Yansane, Todd R Johnson, Gregory W Olson, Shwetha V Kumar, Krishna K Kookal, Aram Kim, Enihomo Obadan-Udoh, Elizabeth Mertz, Kristen Simmons, Joanna Mullins, Ryan Brandon, Muhammad F Walji, Elsbeth Kalenderian

Faculty, Staff and Student Publications

BACKGROUND: Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures).

OBJECTIVE: We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year.

METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For …

A Novel Framework To Estimate Cognitive Impairment Via Finger Interaction With Digital Devices, Ashley A Holmes, Shikha Tripathi, Emily Katz, Ijah Mondesire-Crump, Rahul Mahajan, Aaron Ritter, Teresa Arroyo-Gallego, Luca Giancardo

Faculty, Staff and Student Publications

Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer's Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery …

Quantification Of Infarct Core Signal Using Ct Imaging In Acute Ischemic Stroke, Uma Maria Lal-Trehan Estrada, Grant Meeks, Sergio Salazar-Marioni, Fabien Scalzo, Mudassir Farooqui, Juan Vivanco-Suarez, Santiago Ortega Gutierrez, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved …

Brain Antigens Stimulate Proliferation Of T Lymphocytes With A Pathogenic Phenotype In Multiple Sclerosis Patients, Assaf Gottlieb, Hoai Phuong T Pham, John William Lindsey

Faculty, Staff and Student Publications

A method to stimulate T lymphocytes with a broad range of brain antigens would facilitate identification of the autoantigens for multiple sclerosis and enable definition of the pathogenic mechanisms important for multiple sclerosis. In a previous work, we found that the obvious approach of culturing leukocytes with homogenized brain tissue does not work because the brain homogenate suppresses antigen-specific lymphocyte proliferation. We now report a method that substantially reduces the suppressive activity. We used this non-suppressive brain homogenate to stimulate leukocytes from multiple sclerosis patients and controls. We also stimulated with common viruses for comparison. We measured proliferation, selected the …

Cross-Talk Between Histone Methyltransferases And Demethylases Regulate Rest Transcription During Neurogenesis, Jyothishmathi Swaminathan, Shinji Maegawa, Shavali Shaik, Ajay Sharma, Javiera Bravo-Alegria, Lei Guo, Lin Xu, Arif Harmanci, Vidya Gopalakrishnan

Faculty, Staff and Student Publications

The RE1 Silencing Transcription Factor (REST) is a major regulator of neurogenesis and brain development. Medulloblastoma (MB) is a pediatric brain cancer characterized by a blockade of neuronal specification. REST gene expression is aberrantly elevated in a subset of MBs that are driven by constitutive activation of sonic hedgehog (SHH) signaling in cerebellar granular progenitor cells (CGNPs), the cells of origin of this subgroup of tumors. To understand its transcriptional deregulation in MBs, we first studied control of Rest gene expression during neuronal differentiation of normal mouse CGNPs. Higher Rest expression was observed in proliferating CGNPs compared to differentiating neurons. …

Electron Transfer Dynamics And Electrocatalytic Oxygen Evolution Activities Of The Co3o4 Nanoparticles Attached To Indium Tin Oxide By Self-Assembled Monolayers, Xuan Liu, Qianhong Tian, Yvpei Li, Zixiang Zhou, Jinlian Wang, Shuling Liu, Chao Wang

Faculty, Staff and Student Publications

The Co3O4 nanoparticle-modified indium tin oxide-coated glass slide (ITO) electrodes are successfully prepared using dicarboxylic acid as the self-assembled monolayer through a surface esterification reaction. The ITO-SAM-Co3O4 (SAM = dicarboxylic acid) are active to electrochemically catalyze oxygen evolution reaction (OER) in acid. The most active assembly, with Co loading at 3.31 × 10-8 mol cm-2, exhibits 374 mV onset overpotential and 497 mV overpotential to reach 1 mA cm-2 OER current in 0.1 M HClO4. The electron transfer rate constant (k) is acquired using Laviron's approach, and the results show that k is not affected by the carbon …

A Nomogram For Predicting Upper Urinary Tract Damage Risk In Children With Neurogenic Bladder, Qi Li, Miao Cai, Qingsong Pu, Shengde Wu, Xing Liu, Tao Lin, Dawei He, Jianguo Wen, Guanghui Wei

Faculty, Staff and Student Publications

PURPOSE: To establish a predictive model for upper urinary tract damage (UUTD) in children with neurogenic bladder (NB) and verify its efficacy.

METHODS: A retrospective study was conducted that consisted of a training cohort with 167 NB patients and a validation cohort with 100 NB children. The clinical data of the two groups were compared first, and then univariate and multivariate logistic regression analyses were performed on the training cohort to identify predictors and develop the nomogram. The accuracy and clinical usefulness of the nomogram were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses.

RESULTS: …

Risk Of Alzheimer's Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching, Avram S Bukhbinder, Yaobin Ling, Omar Hasan, Xiaoqian Jiang, Yejin Kim, Kamal N Phelps, Rosemarie E Schmandt, Albert Amran, Ryan Coburn, Srivathsan Ramesh, Qian Xiao, Paul E Schulz

Faculty, Staff and Student Publications

BACKGROUND: Prior studies have found a reduced risk of dementia of any etiology following influenza vaccination in selected populations, including veterans and patients with serious chronic health conditions. However, the effect of influenza vaccination on Alzheimer's disease (AD) risk in a general cohort of older US adults has not been characterized.

OBJECTIVE: To compare the risk of incident AD between patients with and without prior influenza vaccination in a large US claims database.

METHODS: Deidentified claims data spanning September 1, 2009 through August 31, 2019 were used. Eligible patients were free of dementia during the 6-year look-back period and≥65 years …

Fairly Predicting Graft Failure In Liver Transplant For Organ Assigning, Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse …