Medicine and Health Sciences Commons^™

Measuring And Controlling Medical Record Abstraction (Mra) Error Rates In An Observational Study., Maryam Y Garza, Tremaine Williams, Sahiti Myneni, Susan H Fenton, Songthip Ounpraseuth, Zhuopei Hu, Jeannette Lee, Jessica Snowden, Meredith N Zozus, Anita C Walden, Alan E Simon, Barbara Mcclaskey, Sarah G Sanders, Sandra S Beauman, Sara R Ford, Lacy Malloch, Amy Wilson, Lori A Devlin, Leslie W Young

Journal Articles

BACKGROUND: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.

METHODS: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal …

Comprehensive Characterization Of Covid-19 Patients With Repeatedly Positive Sars-Cov-2 Tests Using A Large U.S. Electronic Health Record Database., Xiao Dong, Yujia Zhou, Xiao-Ou Shu, Elmer V Bernstam, Rebecca Stern, David M Aronoff, Hua Xu, Loren Lipworth

Journal Articles

In the absence of genome sequencing, two positive molecular tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible reinfection. Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. The prevalence of chronic medical conditions, symptoms, and severe outcomes related to coronavirus disease 19 (COVID-19) illness were ascertained. The median age of patients was 64.5 years, 40% were Black, and 39% …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry., Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Journal Articles

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Generalized And Transferable Patient Language Representation For Phenotyping With Limited Data., Yuqi Si, Elmer V Bernstam, Kirk Roberts

Journal Articles

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 …

Representation Of Ehr Data For Predictive Modeling: A Comparison Between Umls And Other Terminologies., Laila Rasmy, Firat Tiryaki, Yujia Zhou, Yang Xiang, Cui Tao, Hua Xu, Degui Zhi

Journal Articles

OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning.

MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, …

Covid-19 Testnorm: A Tool To Normalize Covid-19 Testing Names To Loinc Codes., Xiao Dong, Jianfu Li, Ekin Soysal, Jiang Bian, Scott L Duvall, Elizabeth Hanchrow, Hongfang Liu, Kristine E Lynch, Michael Matheny, Karthik Natarajan, Lucila Ohno-Machado, Serguei Pakhomov, Ruth Madeleine Reeves, Amy M Sitapati, Swapna Abhyankar, Theresa Cullen, Jami Deckard, Xiaoqian Jiang, Robert Murphy, Hua Xu

Journal Articles

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) …

Deep Learning In Clinical Natural Language Processing: A Methodical Review., Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, Bo Zhao, Hua Xu

Journal Articles

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.

RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) …

Digilego For Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation, J Rodin, C Timko, S Harris

Journal Articles

Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego …

Enhancing Clinical Concept Extraction With Contextual Embeddings., Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts

Journal Articles

OBJECTIVE: Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText).

MATERIALS AND METHODS: Both off-the-shelf, open-domain embeddings and …

Effects Of A Community Population Health Initiative On Blood Pressure Control In Latinos., James R Langabeer, Timothy D Henry, Carlos Perez Aldana, Larissa Deluna, Nora Silva, Tiffany Champagne-Langabeer

Journal Articles

Background Hypertension remains one of the most important, modifiable cardiovascular risk factors. Yet, the largest minority ethnic group (Hispanics/Latinos) often have different health outcomes and behavior, making hypertension management more difficult. We explored the effects of an American Heart Association-sponsored population health intervention aimed at modifying behavior of Latinos living in Texas. Methods and Results We enrolled 8071 patients, and 5714 (65.7%) completed the 90-day program (58.5 years ±11.7; 59% female) from July 2016 to June 2018. Navigators identified patients with risk factors; initial and final blood pressure ( BP ) readings were performed in the physician's office; and interim …

A Frame-Based Nlp System For Cancer-Related Information Extraction., Yuqi Si, Kirk Roberts

Journal Articles

We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F

Identification Of An Association Of Tnfaip3 Polymorphisms With Matrix Metalloproteinase Expression In Fibroblasts In An Integrative Study Of Systemic Sclerosis-Associated Genetic And Environmental Factors.*, Peng Wei, Yang Yang, Xinjian Guo, Nainan Hei, Syeling Lai, Shervin Assassi, Mengyuan Liu, Filemon Tan, Xiaodong Zhou

Faculty Publications

OBJECTIVE: Systemic sclerosis (SSc) is a fibrotic disease attributed to both genetic susceptibility and environmental factors. This study was undertaken to investigate the associations between SSc-associated genetic variants and the expression of extracellular matrix (ECM) genes in human fibroblasts stimulated with silica particles in time-course and dose-response experiments.

METHODS: A total of 200 fibroblast strains were examined for ECM gene expression after stimulation with silica particles. The fibroblasts were genetically profiled using Immunochip assays and then subjected to whole-genome genotype imputation. Associations of genotypes and gene expression were first analyzed in a Caucasian cohort and then validated in a meta-analysis …

Is Whole-Exome Sequencing An Ethically Disruptive Technology? Perspectives Of Pediatric Oncologists And Parents Of Pediatric Patients With Solid Tumors., Laurence B Mccullough, Melody J Slashinski, Amy L Mcguire, Richard L Street, Christine M Eng, Richard A Gibbs, D William Parsons, Sharon E Plon

Faculty Publications

BACKGROUND: It has been anticipated that physician and parents will be ill prepared or unprepared for the clinical introduction of genome sequencing, making it ethically disruptive.

PROCEDURE: As a part of the Baylor Advancing Sequencing in Childhood Cancer Care study, we conducted semistructured interviews with 16 pediatric oncologists and 40 parents of pediatric patients with cancer prior to the return of sequencing results. We elicited expectations and attitudes concerning the impact of sequencing on clinical decision making, clinical utility, and treatment expectations from both groups. Using accepted methods of qualitative research to analyze interview transcripts, we completed a thematic analysis …

Binomial Regression With A Misclassified Covariate And Outcome., Sheng Luo, Wenyaw Chan, Michelle A Detry, Paul J Massman, R S. Doody

Faculty Publications

Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease-exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach …

Identification Of A Novel Gene On 10q22.1 Causing Autosomal Dominant Retinitis Pigmentosa (Adrp)., Stephen P Daiger, Lori S Sullivan, Sara J Bowne, Daniel C Koboldt, Susan H Blanton, Dianna K Wheaton, Cheryl E Avery, Elizabeth D Cadena, Robert K Koenekoop, Robert S Fulton, Richard K Wilson, George M Weinstock, Richard A Lewis, David G Birch

Faculty Publications

Whole-genome linkage mapping identified a region on chromosome 10q21.3-q22.1 with a maximum LOD score of 3.0 at 0 % recombination in a six-generation family with autosomal dominant retinitis pigmentosa (adRP). All known adRP genes and X-linked RP genes were excluded in the family by a combination of methods. Whole-exome next-generation sequencing revealed a missense mutation in hexokinase 1, HK1 c.2539G > A, p.Glu847Lys, tracking with disease in all affected family members. One severely-affected male is homozygous for this region by linkage analysis and has two copies of the mutation. No other potential mutations were detected in the linkage region nor were …

Immunological Mechanisms Of Extracorporeal Photopheresis In Cutaneous T Cell Lymphoma And Graft Versus Host Disease, Lisa Shiue

Dissertations & Theses (Open Access)

IMMUNOLOGICAL MECHANISMS OF EXTRACORPOREAL PHOTOPHERESIS IN CUTANEOUS T CELL LYMPHOMA AND GRAFT VERSUS HOST DISEASE

Publication No.___________

Lisa Harn-Ging Shiue, B.S.

Supervisory Professor: Madeleine Duvic, M.D.

Extracorporeal photopheresis (ECP) is an effective, low-risk immunomodulating therapy for leukemic cutaneous T cell lymphoma (L-CTCL) and graft versus host disease (GVHD), but whether the mechanism(s) of action in these two diseases is (are) identical or different is unclear. To determine the effects of ECP in vivo, we studied regulatory T cells (T-regs), cytotoxic T lymphocytes (CTLs), and dendritic cells (DCs) by immunofluorescence flow cytometry in 18 L-CTCL and 11 GVHD patients before …

Emotional Climate, Feeding Practices, And Feeding Styles: An Observational Analysis Of The Dinner Meal In Head Start Families, Sheryl O Hughes, Thomas G Power, Maria A Papaioannou, Matthew B Cross, Theresa A Nicklas, Sharon K Hall, Richard M Shewchuk

Faculty Publications

BACKGROUND: A number of studies conducted with ethnically diverse, low-income samples have found that parents with indulgent feeding styles had children with a higher weight status. Indulgent parents are those who are responsive to their child's emotional states but have problems setting appropriate boundaries with their child. Because the processes through which styles impact child weight are poorly understood, the aim of this study was to observe differences in the emotional climate created by parents (including affect, tone of voice, and gestures) and behavioral feeding practices among those reporting different feeding styles on the Caregiver's Feeding Styles Questionnaire. A secondary …