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Search Results: 1 - 10 of 208705 matches for " Peggy L. Peissig "
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Cataract research using electronic health records
Carol J Waudby, Richard L Berg, James G Linneman, Luke V Rasmussen, Peggy L Peissig, Lin Chen, Catherine A McCarty
BMC Ophthalmology , 2011, DOI: 10.1186/1471-2415-11-32
Abstract: Electronic algorithms were used to select individuals with cataracts in the Personalized Medicine Research Project database. These were analyzed for cataract prevalence, age at cataract, and previously identified risk factors.Cataract diagnoses and surgeries, though not type of cataract, were successfully identified using electronic algorithms. Age specific prevalence of both cataract (22% compared to 17.2%) and cataract surgery (11% compared to 5.1%) were higher when compared to the Eye Diseases Prevalence Research Group. The risk factors of age, gender, diabetes, and steroid use were confirmed.Using electronic health records can be a viable and efficient tool to identify cataracts for research. However, using retrospective data from this source can be confounded by historical limits on data availability, differences in the utilization of healthcare, and changes in exposures over time.When considering diseases that impact public health worldwide, few would outrank cataracts. Cataracts are the leading cause of blindness worldwide [1]. Global Burden of Disease 2004 from the World Health Organization ranks cataracts as fourth in disabling conditions in the world following hearing loss, refractive errors, and depression. It estimates the prevalence of moderate and severe disability due to cataracts to be 53.8 million for all ages worldwide [2].While cataracts may be congenital or result from a specific trauma, most cataracts are related to aging. As the age demographic shifts upward in the population, the incidence of age-related cataract will also increase. In the United States it is estimated that 17.2% of those age 40 and older have cataracts, and this rate is projected to increase by 50% by the year 2020 [3]. The prevalence of cataract surgery among Americans aged 40-years and older is estimated at 5.1%, and that is likely to increase by almost 60% by the year 2020 [3]. There is also the suggestion that with the predicted ozone depletion, the rate of cortical catar
Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks
Stephen D. Turner,Richard L. Berg,James G. Linneman,Peggy L. Peissig,Dana C. Crawford,Joshua C. Denny,Dan M. Roden,Catherine A. McCarty,Marylyn D. Ritchie,Russell A. Wilke
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0019586
Abstract: Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h2~0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies
Jie Liu,Chunming Zhang,Catherine McCarty,Peggy Peissig,Elizabeth Burnside,David Page
Computer Science , 2012,
Abstract: Large-scale multiple testing tasks often exhibit dependence, and leveraging the dependence between individual tests is still one challenging and important problem in statistics. With recent advances in graphical models, it is feasible to use them to perform multiple testing under dependence. We propose a multiple testing procedure which is based on a Markov-random-field-coupled mixture model. The ground truth of hypotheses is represented by a latent binary Markov random field, and the observed test statistics appear as the coupled mixture variables. The parameters in our model can be automatically learned by a novel EM algorithm. We use an MCMC algorithm to infer the posterior probability that each hypothesis is null (termed local index of significance), and the false discovery rate can be controlled accordingly. Simulations show that the numerical performance of multiple testing can be improved substantially by using our procedure. We apply the procedure to a real-world genome-wide association study on breast cancer, and we identify several SNPs with strong association evidence.
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Jesse Davis,Vitor Santos Costa,Peggy Peissig,Michael Caldwell,Elizabeth Berg,David Page
Computer Science , 2012,
Abstract: Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies.
Enhancing the Power of Genetic Association Studies through the Use of Silver Standard Cases Derived from Electronic Medical Records
Andrew McDavid, Paul K. Crane, Katherine M. Newton, David R. Crosslin, Wayne McCormick, Noah Weston, Kelly Ehrlich, Eugene Hart, Robert Harrison, Walter A. Kukull, Carla Rottscheit, Peggy Peissig, Elisha Stefanski, Catherine A. McCarty, Rebecca Lynn Zuvich, Marylyn D. Ritchie, Jonathan L. Haines, Joshua C. Denny, Gerard D. Schellenberg, Mariza de Andrade, Iftikhar Kullo, Rongling Li, Daniel Mirel, Andrew Crenshaw, James D. Bowen, Ge Li, Debby Tsuang, Susan McCurry, Linda Teri, Eric B. Larson, Gail P. Jarvik, Chris S. Carlson
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0063481
Abstract: The feasibility of using imperfectly phenotyped “silver standard” samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.
Global trends in breast cancer incidence and mortality
Porter,Peggy L.;
Salud Pública de México , 2009, DOI: 10.1590/S0036-36342009000800003
Abstract: this review highlights the increasing incidence of breast cancer world-wide and the increasing burden of breast cancer deaths experienced by lower-income countries. the causes of increasing incidence have been attributed to changes in the prevalence of reproductive risk factors, lifestyle changes, and genetic and biological differences between ethnic and racial groups. all these factors may contribute, but data linking etiological factors to increased risk in developing countries is lacking. the challenge for lower-income countries is developing effective strategies to reverse the trend of increasing mortality. down-staging of breast cancer by early detection is a promising long-term strategy for preventing disease-related deaths but it is difficult to make the economic investment required to carry out broad screening programs. successful strategies for addressing the growing breast cancer burden will therefore take political will, reliable data, public and medical community awareness, and partnerships between community advocates, governments, non-governmental organizations and biotechnology.
Measuring the antioxidative activities of Queso Fresco after post-packaging high-pressure processing  [PDF]
Moushumi Paul, Jeffrey D. Brewster, Diane L. Van Hekken, Peggy M. Tomasula
Advances in Bioscience and Biotechnology (ABB) , 2012, DOI: 10.4236/abb.2012.34042
Abstract: Some milk-associated proteins are known to be nutritionally valuable and form bioactive peptides that exhibit activity against hypertension and oxidative stress. Consumption of cheeses, such as the popular Hispanic-style cheese Queso Fresco (QF), may be a vehicle for delivery of these milk-associated peptides. This paper describes the effects of high-pressure processing (HPP) on the antioxidative activity (ORAC- FL value) of water-soluble proteins extracted from QF samples. QFs were manufactured according to a commercial-make procedure using pasteurized, homogenized milk, without added starter cultures. The cheese was cut into 45 × 45 × 150 mm3 blocks, double packaged in vacuum bags, and received the following HPP treatments: 200, 400, or 600 MPa for either 0, 5, 10, or 20 min, with warming to an internal temperature of either 22℃ or 40℃ prior to HPP treatment. Results show that the core temperature of the cheese during HPP directly affects the ORAC-FL value. The activities of the lower temperature cheeses are independent of time and pressure, and have a median ORAC-FL value of 27 trolox equivalents (TE). The higher temperature cheeses have higher ORAC-FL values ranging from 21.5 to 96.0 TE; the highest activity corresponded to the cheese held at 400 MPa for the longest time under pressure (20 min). The 600 MPa cheeses increase in activity with increasing time under pressure, but are less active than the control cheese. These results indicate that processing temperature and pressure are important factors in the antioxidative activity of these QF samples and further understanding of the roles of these variables may lead to the manufacture of healthier and more nutritious cheeses and dairy products.
A Robust Signal Classification Scheme for Cognitive Radio
Hanwen Cao,Jürgen Peissig
Mathematics , 2012,
Abstract: This paper presents a robust signal classification scheme for achieving comprehensive spectrum sensing of multiple coexisting wireless systems. It is built upon a group of feature-based signal detection algorithms enhanced by the proposed dimension cancelation (DIC) method for mitigating the noise uncertainty problem. The classification scheme is implemented on our testbed consisting real-world wireless devices. The simulation and experimental performances agree with each other well and shows the e?ectiveness and robustness of the proposed scheme.
An age artificial immune system for order pickings in an AS/RS with multiple I/O stations
K.L. Mak,Peggy S.K. Lau
Lecture Notes in Engineering and Computer Science , 2007,
Abstract:
Order Pickings in an AS/RS with Multiple I/O Stations using an Artificial Immune System with Aging Antibodies
K.L. Mak,Peggy S.K. Lau
Engineering Letters , 2008,
Abstract:
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