Life Sciences Commons^™

A Genome-Wide Association Study Discovers 46 Loci Of The Human Metabolome In The Hispanic Community Health Study/Study Of Latinos, Elena V Feofanova, Han Chen, Yulin Dai, Peilin Jia, Megan L Grove, Alanna C Morrison, Qibin Qi, Martha Daviglus, Jianwen Cai, Kari E North, Cathy C Laurie, Robert C Kaplan, Eric Boerwinkle, Bing Yu

Journal Articles

Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value < 1.2 × 10

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, …

Understanding Spatial Language In Radiology: Representation Framework, Annotation, And Spatial Relation Extraction From Chest X-Ray Reports Using Deep Learning., Surabhi Datta, Yuqi Si, Laritza Rodriguez, Sonya E Shooshan, Dina Demner-Fushman, Kirk Roberts

Journal Articles

Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as …

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%) …

Evidence For Gene-Smoking Interactions For Hearing Loss And Deafness In Japanese American Families, Jia Y Wan, Christina Cataby, Andrew Liem, Emily Jeffrey, Trina M Norden-Krichmar, Deborah Goodman, Stephanie A Santorico, Karen L Edwards

Journal Articles

BACKGROUND: This study investigated the relationship between smoking and hearing loss and deafness (HLD) and whether the relationship is modified by genetic variation. Data for these analyses was from the subset of Japanese American families collected as part of the American Diabetes Association Genetics of Non-insulin Dependent Diabetes Mellitus study. Logistic regression with generalized estimating equations assessed the relationship between HLD and smoking. Nonparametric linkage analysis identified genetic regions harboring HLD susceptibility genes and ordered subset analysis was used to identify regions showing evidence for gene-smoking interactions. Genetic variants within these candidate regions were then each tested for interaction with …

10th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The Annual Postdoctoral Science Symposium (APSS) was initiated on August 4, 2011, by the MD Anderson Postdoctoral Association to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience.

APSS is a scientific symposium organized by postdoctoral fellows from The University of Texas MD Anderson Cancer Center that welcomes submissions and presentations from postdoctoral fellows from all Texas Medical Center affiliated institutions and other Houston area institutions. The APSS provides a professional venue for postdoctoral scientists to develop, clarify and refine their research as result of formal reviews and critiques …

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 …

Diet, Weight Management, Physical Activity And Ovarian & Breast Cancer Risk In Women With, Adriana M Coletta, Susan K Peterson, Leticia A Gatus, Kate J Krause, Susan M Schembre, Susan C Gilchrist, Banu Arun, Y Nancy You, Miguel A Rodriguez-Bigas, Larkin L Strong, Karen H Lu, Karen Basen-Engquist

Journal Articles

INTRODUCTION: Women with pathogenic germline gene variants in

METHODS: We searched Medline, EMBASE, CENTRAL, PubMed, and clinicaltrials.gov up to October 3, 2019. We identified 2775 records and included 21.

RESULTS: There is limited evidence related to these factors and ovarian cancer risk. For breast cancer risk, evidence suggests higher diet quality, adulthood weight-loss of ≥10 pounds, and activity during adolescence and young-adulthood may be linked with decreased risk. Higher meat intake and higher daily energy intake may be linked with increased risk.

CONCLUSIONS: There is not enough evidence to suggest tailored recommendations for dietary habits or weight management among women …