Data Science Commons^™

Helping Frontline Workers In Texas-A Framework For Resource Development, Karima Lalani, Meredith O'Neal, Simone Lee Joannou, Bhanumathi Gopal, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

First responders disproportionately experience occupational stress when compared to the general population, and COVID-19 has exacerbated this stress. The nature of their duties as law enforcement officers, firefighters, and medics exposes them to repeated trauma, increasing their risk of developing a broad array of mental health issues, including post-traumatic stress disorder (PTSD), substance use disorder (SUD), and compassion fatigue. This paper describes the need for resources for frontline workers and provides a framework for creating and implementing resources. A team of interdisciplinary subject matter experts developed two major resources. The first resource was a 24/7 helpline to support first responders …

Privacy-Preserving Federated Genome-Wide Association Studies Via Dynamic Sampling, Xinyue Wang, Leonard Dervishi, Wentao Li, Erman Ayday, Xiaoqian Jiang, Jaideep Vaidya

Faculty, Staff and Student Publications

MOTIVATION: Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS.

RESULTS: This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We …

Web-Grading-A Tool To Test Personal Grading Of Renal And Prostate Cancer, Glen Kristiansen, Matthias Schmid, Lars Egevad, Hemamali Samaratunga, Murali Varma, Kaan Inam, Hans-Jürgen Thiesen, Brett Delahunt, Yulin Dai

Faculty, Staff and Student Publications

Only a few pathologists have the opportunity to verify their personal grading through objective assessment. This study introduces a web-based grading platform to facilitate and validate the grading of renal cell carcinoma and prostate cancer. Two representative images of two clinically annotated cohorts of 100 cases each of prostate and renal cell carcinoma were used. Each participant was asked to grade a tumor series utilizing a three tiered grading system. Finally, a Kaplan-Meier curve was drawn, and the log-rank test was used for statistical testing of the p-value. The grading of 22 participants (68%) achieved prognostic significance. Further analysis highlighted …

Discoverpath: A Knowledge Refinement And Retrieval System For Interdisciplinarity On Biomedical Research, Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath …

Evolving Availability And Standardization Of Patient Attributes For Matching, Yu Deng, Lacey P Gleason, Adam Culbertson, Xiaotian Chen, Elmer V Bernstam, Theresa Cullen, Ramkiran Gouripeddi, Christopher Harle, David F Hesse, Jacob Kean, John Lee, Tanja Magoc, Daniella Meeker, Toan Ong, Jyotishman Pathak, Marc Rosenman, Laura K Rusie, Akash J Shah, Lizheng Shi, Aaron Thomas, William E Trick, Shaun Grannis, Abel Kho

Faculty, Staff and Student Publications

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes …

A Case Report: A Patient Rescued By Va-Ecmo After Cardiac Arrest Triggered By Trigeminocardiac Reflex After Nasal Surgery, Xu Zhang, Bin Sun, Chen Pac-Soo, Daqing Ma, Liwei Wang

Faculty, Staff and Student Publications

Rationale:

Cardiac arrest (CA) caused by trigeminocardiac reflex (TCR) after endoscopic nasal surgery is rare. Hence, when a patient suffers from TCR induced CA in the recovery room, most doctors may not be able to find the cause in a short time, and standard cardiopulmonary resuscitation and resuscitation measures may not be effective. Providing circulatory assistance through venous-arterial extracorporeal membrane oxygenation (VA-ECMO) can help healthcare providers gain time to identify the etiology and initiate symptom-specific treatment.

Patient concerns:

We report a rare case of CA after endoscopic nasal surgery treated with VA-ECMO.

Diagnoses:

We excluded myocardial infarction, pulmonary embolism, allergies, …

Using Artificial Intelligence To Learn Optimal Regimen Plan For Alzheimer’S Disease, Kritib Bhattarai, Sivaraman Rajaganapathy, Trisha Das, Yejin Kim, Yongbin Chen, Alzheimer’S Disease Neuroimaging Initiative, Australian Imaging Biomarkers And Lifestyle Flagship Study Of Ageing, Qiying Dai, Xiaoyang Li, Xiaoqian Jiang, Nansu Zong

Faculty, Staff and Student Publications

BACKGROUND: Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

OBJECTIVE: Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

METHODS: In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). …

Mapping The Delineation Of Practice To The Amia Foundational Domains For Applied Health Informatics, Todd R Johnson, Eta S Berner, Sue S Feldman, Josette Jones, Annette L Valenta, Damian Borbolla, Gloria Deckard, Laverne Manos

Faculty, Staff and Student Publications

OBJECTIVE: This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice.

MATERIALS AND METHODS: Using an iterative …

Systematic Investigation Of The Homology Sequences Around The Human Fusion Gene Breakpoints In Pan-Cancer - Bioinformatics Study For A Potential Link To Mmej, Pora Kim, Himansu Kumar, Chengyuan Yang, Ruihan Luo, Jiajia Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to …

Mettl14 Is A Chromatin Regulator Independent Of Its Rna N6-Methyladenosine Methyltransferase Activity, Xiaoyang Dou, Lulu Huang, Yu Xiao, Chang Liu, Yini Li, Xinning Zhang, Lishan Yu, Ran Zhao, Lei Yang, Chuan Chen, Xianbin Yu, Boyang Gao, Meijie Qi, Yawei Gao, Bin Shen, Shuying Sun, Chuan He, Jun Liu

Faculty, Staff and Student Publications

METTL3 and METTL14 are two components that form the core heterodimer of the main RNA m6A methyltransferase complex (MTC) that installs m6A. Surprisingly, depletion of METTL3 or METTL14 displayed distinct effects on stemness maintenance of mouse embryonic stem cell (mESC). While comparable global hypo-methylation in RNA m6A was observed in Mettl3 or Mettl14 knockout mESCs, respectively. Mettl14 knockout led to a globally decreased nascent RNA synthesis, whereas Mettl3 depletion resulted in transcription upregulation, suggesting that METTL14 might possess an m6A-independent role in gene regulation. We found that METTL14 colocalizes with the repressive H3K27me3 modification. Mechanistically, METTL14, but not METTL3, binds …

Pathogen-Driven Crispr Screens Identify Trex1as A Regulator Of Dna Self-Sensing During Influenza Virus Infection, Cason R King, Yiping Liu, Katherine A Amato, Grace A Schaack, Clayton Mickelson, Autumn E Sanders, Tony Hu, Srishti Gupta, Ryan A Langlois, Judith A Smith, Andrew Mehle

Faculty, Staff and Student Publications

Host:pathogen interactions dictate the outcome of infection, yet the limitations of current approaches leave large regions of this interface unexplored. Here, we develop a novel fitness-based screen that queries factors important during the middle to late stages of infection. This is achieved by engineering influenza virus to direct the screen by programming dCas9 to modulate host gene expression. Our genome-wide screen for pro-viral factors identifies the cytoplasmic DNA exonuclease TREX1. TREX1 degrades cytoplasmic DNA to prevent inappropriate innate immune activation by self-DNA. We reveal that this same process aids influenza virus replication. Infection triggers release of mitochondrial DNA into the …

Collagene Enables Privacy-Aware Federated And Collaborative Genomic Data Analysis, Wentao Li, Miran Kim, Kai Zhang, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 …

An Electronic Origin Of Charge Order In Infinite-Layer Nickelates, Hanghui Chen, Yi-Feng Yang, Guang-Ming Zhang, Hongquan Liu

Faculty, Staff and Student Publications

A charge order (CO) with a wavevector [Formula: see text] is observed in infinite-layer nickelates. Here we use first-principles calculations to demonstrate a charge-transfer-driven CO mechanism in infinite-layer nickelates, which leads to a characteristic Ni1+-Ni2+-Ni1+ stripe state. For every three Ni atoms, due to the presence of near-Fermi-level conduction bands, Hubbard interaction on Ni-d orbitals transfers electrons on one Ni atom to conduction bands and leaves electrons on the other two Ni atoms to become more localized. We further derive a low-energy effective model to elucidate that the CO state arises from a delicate competition between Hubbard interaction on Ni-d …

Crabr-Net: A Contextual Relational Attention-Based Recognition Network For Remote Sensing Scene Objective, Ningbo Guo, Mingyong Jiang, Lijing Gao, Yizhuo Tang, Jinwei Han, Xiangning Chen

Faculty, Staff and Student Publications

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the "summation" way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a …

Developing Electronic Clinical Quality Measures To Assess The Cancer Diagnostic Process, Daniel R Murphy, Andrew J Zimolzak, Divvy K Upadhyay, Li Wei, Preeti Jolly, Alexis Offner, Dean F Sittig, Saritha Korukonda, Riyaa Murugaesh Rekha, Hardeep Singh

Faculty, Staff and Student Publications

OBJECTIVE: Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance.

MATERIALS AND METHODS: We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of …

Federated Generalized Linear Mixed Models For Collaborative Genome-Wide Association Studies, Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (

Systematic Design And Data-Driven Evaluation Of Social Determinants Of Health Ontology (Sdoho), Yifang Dang, Fang Li, Xinyue Hu, Vipina K Keloth, Meng Zhang, Sunyang Fu, Muhammad F Amith, J Wilfred Fan, Jingcheng Du, Evan Yu, Hongfang Liu, Xiaoqian Jiang, Hua Xu, Cui Tao

Faculty, Staff and Student Publications

Objective: Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way.

Material and methods: Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and …

A Lifecycle Framework Illustrates Eight Stages Necessary For Realizing The Benefits Of Patient-Centered Clinical Decision Support, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Priyanka Desai, Rina Dhopeshwarkar, James Swiger, Edwin A Lomotan, Angela Dobes, Prashila Dullabh

Faculty, Staff and Student Publications

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following stages: Computable Clinical Knowledge, Patient-specific Inference, Information Delivery, Clinical Decision, Patient Behaviors, Health Outcomes, Aggregate Data, and patient-centered outcomes research (PCOR) Evidence. Using this idealized framework reminds key stakeholders that …

Identifying Contributing Factors Associated With Dental Adverse Events Through A Pragmatic Electronic Health Record-Based Root Cause Analysis, Elsbeth Kalenderian, Suhasini Bangar, Alfa Yansane, Duong Tran, Emily Sedlock, Yan Xiao, Janelle Urata, Greg Olson, Amy Franklin, Krishna Kookal, Ana Ibarra-Noriega, Sayali Tungare, Oluwabunmi Tokede, Heiko Spallek, Joel M White, Muhammad F Walji

Faculty, Staff and Student Publications

OBJECTIVE: This study assessed contributing factors associated with dental adverse events (AEs).

METHODS: Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems. Based on information captured in the electronic health record, we analyzed harmful AEs to assess potential contributing factors; harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. We classified potential contributing factors according …

Knowledge Representation And Management 2022: Findings In Ontology Development And Applications, Jean Charlet, Licong Cui, Section Editors For The Imia Yearbook Section On Knowledge Representation And Management

Faculty, Staff and Student Publications

OBJECTIVES: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

METHODS: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.

RESULTS: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, …

Federated Generalized Linear Mixed Models For Collaborative Genome-Wide Association Studies, Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges. Confounding factors like population stratification should be carefully modeled across sites. In addition, it is crucial to consider disease etiology using flexible models to prevent biases. Privacy protections for participants pose another significant challenge. Here, we propose distributed Mixed Effects Genome-wide Association study (dMEGA), a method that enables federated generalized linear mixed model-based association testing across multiple sites without explicitly sharing genotype and phenotype data. dMEGA employs a reference projection to …

Surface-Doped Zinc Gallate Colloidal Nanoparticles Exhibit Ph-Dependent Radioluminescence With Enhancement In Acidic Media, Navadeep Shrivastava, Jessa Guffie, Tamela L Moore, Burak Guzelturk, Amar S Kumbhar, Jianguo Wen, Zhiping Luo

Faculty, Staff and Student Publications

As abnormal acidic pH symbolizes dysfunctions of cells, it is highly desirable to develop pH-sensitive luminescent materials for diagnosing disease and imaging-guided therapy using high-energy radiation. Herein, we explored near-infrared-emitting Cr-doped zinc gallate ZnGa2O4 nanoparticles (NPs) in colloidal solutions with different pH levels under X-ray excitation. Ultrasmall NPs were synthesized via a facile hydrothermal method by controlling the addition of ammonium hydroxide precursor and reaction time, and structural characterization revealed Cr dopants on the surface of NPs. The synthesized NPs exhibited different photoluminescence and radioluminescence mechanisms, confirming the surface distribution of activators. It was observed that the colloidal NPs emit …

The Impact Framework And Implementation For Accessible In Silico Clinical Phenotyping In The Digital Era, Andrew Wen, Huan He, Sunyang Fu, Sijia Liu, Kurt Miller, Liwei Wang, Kirk E Roberts, Steven D Bedrick, William R Hersh, Hongfang Liu

Faculty, Staff and Student Publications

Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping …

Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Yan Chu, Kaichen Tang, Yu-Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I Savitz, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

BACKGROUND: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct …

The Screening Of The Protective Antigens Of Aeromonas Hydrophila Using The Reverse Vaccinology Approach: Potential Candidates For Subunit Vaccine Development, Ting Zhang, Minying Zhang, Zehua Xu, Yang He, Xiaoheng Zhao, Hanliang Cheng, Xiangning Chen, Jianhe Xu, Zhujin Ding

Faculty, Staff and Student Publications

The threat of bacterial septicemia caused by Aeromonas hydrophila infection to aquaculture growth can be prevented through vaccination, but differences among A. hydrophila strains may affect the effectiveness of non-conserved subunit vaccines or non-inactivated A. hydrophila vaccines, making the identification and development of conserved antigens crucial. In this study, a bioinformatics analysis of 4268 protein sequences encoded by the A. hydrophila J-1 strain whole genome was performed based on reverse vaccinology. The specific analysis included signal peptide prediction, transmembrane helical structure prediction, subcellular localization prediction, and antigenicity and adhesion evaluation, as well as interspecific and intraspecific homology comparison, thereby screening …

The Impact Of Covid-19 On The Financial Performance Of Largest Teaching Hospitals, Karima Lalani, Jeffrey Helton, Francine R Vega, Marylou Cardenas-Turanzas, Tiffany Champagne-Langabeer, James R Langabeer

Faculty, Staff and Student Publications

The COVID-19 pandemic disrupted hospital operations. Anecdotal evidence suggests financial performance likewise suffered, yet little empirical research supports this claim. This study aimed to explore the impact of the pandemic on the financial performance of the most prominent academic hospitals in the United States. Data from the 115 largest major teaching hospitals in the United States were extracted from the American Hospital Directory for three years (2019-2021). We hypothesized that the year and region would moderate the relationship between a hospital's return on assets (financial performance) and specific operational variables. We found evidence through descriptive statistics and multivariate moderated regressions …

Fast Inference Of Genetic Recombination Rates In Biobank Scale Data, Ardalan Naseri, William Yue, Shaojie Zhang, Degui Zhi

Faculty, Staff and Student Publications

Although rates of recombination events across the genome (genetic maps) are fundamental to genetic research, the majority of current studies only use one standard map. There is evidence suggesting population differences in genetic maps, and thus estimating population-specific maps, are of interest. Although the recent availability of biobank-scale data offers such opportunities, current methods are not efficient at leveraging very large sample sizes. The most accurate methods are still linkage disequilibrium (LD)-based methods that are only tractable for a few hundred samples. In this work, we propose a fast and memory-efficient method for estimating genetic maps from population genotyping data. …

Multi-Task Learning With Dynamic Re-Weighting To Achieve Fairness In Healthcare Predictive Modeling, Can Li, Sirui Ding, Na Zou, Xia Hu, Xiaoqian Jiang, Kai Zhang

Faculty, Staff and Student Publications

The emphasis on fairness in predictive healthcare modeling has increased in popularity as an approach for overcoming biases in automated decision-making systems. The aim is to guarantee that sensitive characteristics like gender, race, and ethnicity do not influence prediction outputs. Numerous algorithmic strategies have been proposed to reduce bias in prediction results, mitigate prejudice toward minority groups and promote prediction fairness. The goal of these strategies is to ensure that model prediction performance does not exhibit significant disparity among sensitive groups. In this study, we propose a novel fairness-achieving scheme based on multitask learning, which fundamentally differs from conventional fairness-achieving …

Weakly Supervised Spatial Relation Extraction From Radiology Reports, Surabhi Datta, Kirk Roberts

Faculty, Staff and Student Publications

OBJECTIVE: Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.

MATERIALS AND METHODS: Our weak supervision approach is based on data programming that uses rules (or labeling functions) relying on domain-specific dictionaries and radiology language characteristics to generate weak labels. The labels correspond to different spatial relations that are critical to understanding radiology reports. These weak labels are then used to fine-tune a pretrained Bidirectional Encoder Representations from …

Minimal Positional Substring Cover Is A Haplotype Threading Alternative To Li And Stephens Model, Ahsan Sanaullah, Degui Zhi, Shaojie Zhang

Faculty, Staff and Student Publications

The Li and Stephens (LS) hidden Markov model (HMM) models the process of reconstructing a haplotype as a mosaic copy of haplotypes in a reference panel. For small panels, the probabilistic parameterization of LS enables modeling the uncertainties of such mosaics. However, LS becomes inefficient when sample size is large, because of its linear time complexity. Recently the PBWT, an efficient data structure capturing the local haplotype matching among haplotypes, was proposed to offer a fast method for giving some optimal solution (Viterbi) to the LS HMM. Previously, we introduced the minimal positional substring cover (MPSC) problem as an alternative …