Publications by authors named "Tiffany J Callahan"

32 Publications

A biomedically oriented automatically annotated Twitter COVID-19 dataset.

Genomics Inform 2021 Sep 30;19(3):e21. Epub 2021 Sep 30.

Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.
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http://dx.doi.org/10.5808/gi.21011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510871PMC
September 2021

A Biomedically oriented automatically annotated Twitter COVID-19 Dataset.

ArXiv 2021 Jul 27. Epub 2021 Jul 27.

Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303 USA.

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontraditional sources of clinical data to characterize the disease in near real-time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-COVID). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations do not generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328063PMC
July 2021

Knowledge-Based Biomedical Data Science.

Annu Rev Biomed Data Sci 2020 Jul 7;3:23-41. Epub 2020 Apr 7.

Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA.

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.
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http://dx.doi.org/10.1146/annurev-biodatasci-010820-091627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095730PMC
July 2020

Cyclooxygenase inhibitor use is associated with increased COVID-19 severity.

medRxiv 2021 Apr 20. Epub 2021 Apr 20.

Background: Cyclooxygenase (COX) inhibitors including non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community acquired pneumonia and other respiratory tract infections (RTIs). Conclusive data are not available about potential beneficial or adverse effects of COX inhibitors on COVID-19 patients.

Methods: We conducted a retrospective, multi-center observational study by leveraging the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative (N3C). Potential associations of eight COX inhibitors with COVID-19 severity were assessed using ordinal logistic regression (OLR) on treatment with the medication in question after matching by treatment propensity as predicted by age, race, ethnicity, gender, smoking status, comorbidities, and BMI. Cox proportional hazards analysis was used to estimate the correlation of medication use with morbidity for eight subcohorts defined by common indications for COX inhibitors.

Results: OLR revealed statistically significant associations between use of any of five COX inhibitors and increased severity of COVID-19. For instance, the odds ratio of aspirin use in the osteoarthritis cohort (n=2266 patients) was 3.25 (95% CI 2.76 - 3.83). Aspirin and acetaminophen were associated with increased mortality.

Conclusions: The association between use of COX inhibitors and COVID-19 severity was consistent across five COX inhibitors and multiple indication subcohorts. Our results align with earlier reports associating NSAID use with complications in RTI patients. Further research is needed to characterize the precise risk of individual COX inhibitors in COVID-19 patients.
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http://dx.doi.org/10.1101/2021.04.13.21255438DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077581PMC
April 2021

Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information.

medRxiv 2021 Mar 26. Epub 2021 Mar 26.

Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely "Long COVID", but also "COVID-19 syndrome (PACS)" or, "post-acute sequelae of SARS-CoV-2 infection (PASC)". In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.
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http://dx.doi.org/10.1101/2021.03.20.21253896DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010765PMC
March 2021

Community Approaches for Integrating Environmental Exposures into Human Models of Disease.

Environ Health Perspect 2020 12 28;128(12):125002. Epub 2020 Dec 28.

Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA.

Background: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.

Objectives: Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.

Discussion: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.
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http://dx.doi.org/10.1289/EHP7215DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769179PMC
December 2020

The Human Phenotype Ontology in 2021.

Nucleic Acids Res 2021 01;49(D1):D1207-D1217

Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
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http://dx.doi.org/10.1093/nar/gkaa1043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7778952PMC
January 2021

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Patterns (N Y) 2021 Jan 9;2(1):100155. Epub 2020 Nov 9.

Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
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http://dx.doi.org/10.1016/j.patter.2020.100155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649624PMC
January 2021

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.

bioRxiv 2020 Aug 18. Epub 2020 Aug 18.

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

Bigger Picture: An effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.
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http://dx.doi.org/10.1101/2020.08.17.254839DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444288PMC
August 2020

Applying knowledge-driven mechanistic inference to toxicogenomics.

Toxicol In Vitro 2020 Aug 6;66:104877. Epub 2020 May 6.

University of Colorado Anschutz Medical Campus, Computational Bioscience / Interdisciplinary Quantitative Biology, Denver, CO 80045, USA.

When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology, and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.
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http://dx.doi.org/10.1016/j.tiv.2020.104877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306473PMC
August 2020

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

NPJ Digit Med 2019 2;2. Epub 2019 May 2.

The Jackson Laboratory for Genomic Medicine, Farmington CT 06032, USA.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
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http://dx.doi.org/10.1038/s41746-019-0110-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527418PMC
May 2019

A Data Element-Function Conceptual Model for Data Quality Checks.

EGEMS (Wash DC) 2019 Apr 23;7(1):17. Epub 2019 Apr 23.

Department of Biomedical Informatics, Columbia University, US.

Introduction: In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives.

Methods: The model defines a "data element", the primary focus of the check, and a "function", the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Permanente's Center for Effectiveness and Safety Research (CESR).

Results: The model was able to classify all checks. A total of 751 unique data elements and 24 unique functions were extracted. The top five frequent data element-function pairings for OHDSI were Person-Count (55 checks), Insurance-Distribution (17), Medication-Count (16), Condition-Count (14), and Observations-Count (13); for CESR, they were Medication-Variable Type (175), Medication-Missing (172), Medication-Existence (152), Medication-Count (127), and Socioeconomic Factors-Variable Type (114).

Conclusions: This study shows the efficacy of the data element-function conceptual model for classifying DQ checks, demonstrates early promise of NLP-assisted knowledge acquisition, and reveals the great heterogeneity in the focus in DQ checks, confirming variation in intrinsic checks and use-case specific "fitness-for-use" checks.
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http://dx.doi.org/10.5334/egems.289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484368PMC
April 2019

Open Agile text mining for bioinformatics: the PubAnnotation ecosystem.

Bioinformatics 2019 11;35(21):4372-4380

Computational Bioscience Program, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.

Motivation: Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users.

Results: This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes.

Availability And Implementation: The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.
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http://dx.doi.org/10.1093/bioinformatics/btz227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821251PMC
November 2019

Murine trophoblast-derived and pregnancy-associated exosome-enriched extracellular vesicle microRNAs: Implications for placenta driven effects on maternal physiology.

PLoS One 2019 7;14(2):e0210675. Epub 2019 Feb 7.

Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO, United States of America.

The role of extracellular vesicles (EVs), specifically exosomes, in intercellular communication likely plays a key role in placental orchestration of pregnancy and maternal immune sensing of the fetus. While murine models are powerful tools to study pregnancy and maternal-fetal immune interactions, in contrast to human placental exosomes, the content of murine placental and pregnancy exosomes remains largely understudied. Using a recently developed in vitro culture technique, murine trophoblast stem cells derived from B6 mice were differentiated into syncytial-like cells. EVs from the conditioned media, as well as from pregnant and non-pregnant sera, were enriched for exosomes. The RNA composition of these murine trophoblast-derived and pregnancy-associated exosome-enriched-EVs (ExoE-EVs) was determined using RNA-sequencing analysis and expression levels confirmed by qRT-PCR. Differentially abundant miRNAs were detected in syncytial differentiated ExoE-EVs, particularly from the X chromosome cluster (mmu-miR-322-3p, mmu-miR-322-5p, mmu-miR-503-5p, mmu-miR-542-3p, and mmu-miR-450a-5p). These were confirmed to be increased in pregnant mouse sera ExoE-EVs by qRT-PCR analysis. Interestingly, fifteen miRNAs were only present within the pregnancy-derived ExoE-EVs compared to non-pregnant controls. Mmu-miR-292-3p and mmu-miR-183-5p were noted to be some of the most abundant miRNAs in syncytial ExoE-EVs and were also present at higher levels in pregnant versus non-pregnant sera ExoE-EVs. The bioinformatics tool, MultiMir, was employed to query publicly available databases of predicted miRNA-target interactions. This analysis reveals that the X-chromosome miRNAs are predicted to target ubiquitin-mediated proteolysis and intracellular signaling pathways. Knowing the cargo of placental and pregnancy-specific ExoE-EVs as well as the predicted biological targets informs studies using murine models to examine not only maternal-fetal immune interactions but also the physiologic consequences of placental-maternal communication.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210675PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366741PMC
October 2019

Data Science for Child Health.

J Pediatr 2019 05 25;208:12-22. Epub 2019 Jan 25.

Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO; Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO.

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http://dx.doi.org/10.1016/j.jpeds.2018.12.041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486872PMC
May 2019

Three Dimensions of Reproducibility in Natural Language Processing.

LREC Int Conf Lang Resour Eval 2018 May;2018:156-165

Computational Bioscience Program, University of Colorado School of Medicine.

Despite considerable recent attention to problems with reproducibility of scientific research, there is a striking lack of agreement about the definition of the term. That is a problem, because the lack of a consensus definition makes it difficult to compare studies of reproducibility, and thus to have even a broad overview of the state of the issue in natural language processing. This paper proposes an ontology of reproducibility in that field. Its goal is to enhance both future research and communication about the topic, and retrospective meta-analyses. We show that three dimensions of reproducibility, corresponding to three kinds of claims in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a , of a , and of a Three biomedical natural language processing papers by the authors of this paper are analyzed with respect to these dimensions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998676PMC
May 2018

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks.

EGEMS (Wash DC) 2017 Jun 12;5(1). Epub 2017 Jun 12.

Department of Pediatrics, University of Colorado Denver Anschutz Medical Campus.

Objective: To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations.

Methods: Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficult-to-map DQ checks were discussed with research team members until consensus was achieved.

Results: Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories.

Discussion: Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences.

Conclusion: A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks.
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http://dx.doi.org/10.5334/egems.223DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982846PMC
June 2017

Screening for postpartum anxiety: A quality improvement project to promote the screening of women suffering in silence.

Midwifery 2018 Jul 3;62:161-170. Epub 2018 Apr 3.

Louise Herrington School of Nursing, Baylor University, Dallas, TX 75246, United States . Electronic address:

Background: Postpartum anxiety is a mental health problem that has largely been ignored by maternity care providers despite an estimated incidence as high as 28.9%. Though postpartum anxiety may or may not be accompanied by depression, and while screening for postpartum depression has become more common place, postpartum anxiety is often not assessed or addressed.

Purpose: The purpose of this pilot quality improvement project was to implement a screening, treatment and referral program for postpartum anxiety in the birth centre environment.

Procedures: Midwives from 10 geographically diverse birth centres, and all members of the American Association of Birth Centres, were recruited to participate in the project. An online video was developed which detailed postpartum anxiety, screening through use of the anxiety subscale of the Edinburgh Postnatal Depression Scale and a toolkit for treatment and/or referral for screen positive patients. Participants entered patient scores into the Perinatal Data Registry of the American Association of Birth Centres. Individual interviews of midwives were conducted following the 10-week pilot period.

Main Findings: There were a total of 387 participants across 9 participating sites. Among all screened participants with follow-up data, (n = 382), 9.69% (n = 37) were lost to follow-up. Among all participants screened with the Edinburgh Postpartum Depression Scale -3A and Edinburgh Postpartum Depression Scale (n = 318), 12.58% (n = 40) had a positive Edinburgh Postpartum Depression Scale -3A score of greater than six. Of all screened participants with an Edinburgh Postpartum Depression Scale score, 15 (6.98%) had a Edinburgh Postpartum Depression Scale score of less than 12 and an Edinburgh Postpartum Depression Scale -3A score greater than six, and would have not received follow up care if only screened for postpartum depression. Midwife participants expressed heightened awareness of the need to screen and felt screening was easy to integrate into clinical practice.

Conclusions: The Edinburgh Postpartum Depression Scale -3A is a valid, easy-to-use tool which should be considered for use in clinical practice. Modification of the electronic health record can serve as an important impetus triggering screening and treatment. It is important that clinicians are educated on the prevalence of postpartum anxiety, its risk factors, symptoms and implications.
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http://dx.doi.org/10.1016/j.midw.2018.03.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8040026PMC
July 2018

Nature and scope of certified nurse-midwifery practice: A workforce study.

J Clin Nurs 2018 Nov 20;27(21-22):4000-4017. Epub 2018 Jun 20.

Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado.

Aims And Objectives: To describe the nature and scope of nurse-midwifery practice in Texas and to determine legislative priorities and practice barriers.

Background: Across the globe, midwives are the largest group of maternity care providers despite little known about midwifery practice. With a looming shortage of midwives, there is a pressing need to understand midwives' work environment and scope of practice.

Design: Mixed methods research utilising prospective descriptive survey and interview.

Methods: An online survey was administered to nurse-midwives practicing in the state of Texas (N = 449) with a subset (n = 10) telephone interviewed. Descriptive and inferential statistics and content analysis was performed.

Results: The survey was completed by 141 midwives with eight interviewed. Most were older, Caucasian and held a master's degree. A majority worked full-time, were in clinical practice in larger urban areas and were employed by a hospital or physician group. Care was most commonly provided for Hispanic and White women; approximately a quarter could care for greater numbers of patients. Most did not clinically teach midwifery students. Physician practice agreements were believed unnecessary and prescriptive authority requirements restrictive. Legislative issues were typically followed through the professional organisation or social media sites; most felt a lack of competence to influence health policy decisions. While most were satisfied with current clinical practice, a majority planned a change in the next 3 to 5 years.

Conclusions: An ageing midwifery workforce, not representative of the race/ethnicity of the populations served, is underutilised with practice requirements that limit provision of services. Health policy changes are needed to ensure unrestricted practice.

Relevance To Clinical Practice: Robust midwifery workforce data are needed as well as a midwifery board which tracks availability and accessibility of midwives. Educators should consider training models promoting long-term service in underserved areas, and development of skills crucial for impacting health policy change.
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http://dx.doi.org/10.1111/jocn.14489DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992184PMC
November 2018

Utilization of a biomedical device (VeinViewer ) to assist with peripheral intravenous catheter (PIV) insertion for pediatric nurses.

J Spec Pediatr Nurs 2018 04 10;23(2):e12208. Epub 2018 Feb 10.

Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, USA.

Purpose: Vascular access in pediatric patients can be challenging even with the currently available technological resources. This nurse-driven research study explored time, cost, and resources for intravenous access to determine if a biomedical device, VeinViewer Vision, would facilitate improvements in pediatric access. In addition, this study looked at nurse perceptions of skills and confidence around intravenous insertion and if the use of the VeinViewer impacted these perceptions. Literature examining pediatric intravenous access success rates compared with nurse perceived skills and confidence is lacking.

Design: Nonblinded randomized control trial of pediatric nurses working in an acute care hospital setting.

Methods: A preliminary needs assessment solicited feedback from nurses regarding their practice, perceived skills, and confidence with placing peripheral intravenous catheters (PIVs). Due to the results of the preliminary needs assessment, a research study was designed and 40 nurses were recruited to participate. The nurses were randomized into either a VeinViewer or standard practice group. Nurse participants placed intravenous catheters on hospitalized pediatric patients using established procedures while tracking data for the study.

Results: Needs assessment showed a majority of nurses felt a biomedical device would be helpful in building their intravenous insertion skills and their confidence. The study results did not demonstrate any clinically significant differences between VeinViewer use and standard practice for intravenous catheter insertion in pediatric patients for success of placement, number of attempts, or overall cost. In addition, no difference was noted between nurses in either group on perceived skills or confidence with insertion of PIVs.

Practice Implications: The ongoing need for resources focused on building nurse skills and confidence for PIV insertion was highlighted and organizations should continue to direct efforts toward developing skills and competency for staff that are responsible for pediatric vascular access. This study illustrates the importance of data-driven decision-making for expensive hospital-funded equipment purchases. This nursing led research study highlights how perceptions do not always align with outcomes. The lessons gleaned from this study may aid in decision-making around pediatric intravenous access practice.
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http://dx.doi.org/10.1111/jspn.12208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056604PMC
April 2018

Semantic Relations in Compound Nouns: Perspectives from Inter-Annotator Agreement.

Stud Health Technol Inform 2017 ;245:644-648

Computational Bioscience Program, University of Colorado School of Medicine, Aurora, Colorado 80045, USA.

Semantic relations have been studied for decades without yet reaching consensus on the set of these relations. However, biomedical language processing and ontologies rely on these relations, so it is important to be able to evaluate their suitability. In this paper we examine the role of inter-annotator agreement in choosing between competing proposals regarding the set of such relations. The experiments consisted of labeling the semantic relations between two elements of noun-noun compounds (e.g. cell migration). Two judges annotated a dataset of terms from the biomedical domain using two competing sets of relations and analyzed the inter-annotator agreement. With no training and little documentation, agreement on this task was fairly high and disagreements were consistent. The results support the utility of the relation-based approach to semantic representation.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781293PMC
June 2018

OWL-NETS: Transforming OWL Representations for Improved Network Inference.

Pac Symp Biocomput 2018 ;23:133-144

Computational Bioscience Program, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA,

Our knowledge of the biological mechanisms underlying complex human disease is largely incomplete. While Semantic Web technologies, such as the Web Ontology Language (OWL), provide powerful techniques for representing existing knowledge, well-established OWL reasoners are unable to account for missing or uncertain knowledge. The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge. Therefore, robust methods which facilitate inductive inference on rich OWL-encoded knowledge are needed. Here, we propose OWL-NETS (NEtwork Transformation for Statistical learning), a novel computational method that reversibly abstracts OWL-encoded biomedical knowledge into a network representation tailored for network inference. Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant hypotheses. Further, the lossless transformation of OWL-NETS allows for seamless integration of inferred edges back into the original knowledge base, extending its coverage and completeness.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737627PMC
August 2018

Early Initiation of Newborn Individualized Developmental Care and Assessment Program (NIDCAP) Reduces Length of Stay: A Quality Improvement Project.

J Pediatr Nurs 2017 Jan - Feb;32:59-63. Epub 2016 Dec 5.

University of Colorado Anschutz Medical Campus, College of Nursing, Aurora, CO 80045, United States; Children's Hospital Colorado, 13123 East 16th Avenue, Aurora, CO 80045, United States.

Infants born at ≤32weeks gestation are at risk of developmental delays. Review of the literature indicates NIDCAP improves parental satisfaction, minimizes developmental delays, and decreases length of stay, thus reducing cost of hospitalization. Half (50.6%) of the infants admitted to this 84-bed Level IV Neonatal Intensive Care Unit (NICU) with a gestational age of ≤32weeks were referred for NIDCAP. The specific aims of this quality improvement project were to 1) compare the age at discharge for infants meeting inclusion criteria enrolled in NIDCAP with the age at discharge for those eligible infants not enrolled in NIDCAP; and 2) investigate the timing of initiation of NIDCAP (e.g., within six days of admission) on age at discharge. During the 12month period of data collection, infants enrolled in NIDCAP (M=27.85weeks, SD=1.86) were 2.02weeks younger than those not enrolled in NIDCAP (M=29.87weeks, SD=2.49), and were 2.32weeks older at discharge (M=38.28weeks, SD=5.10) than those not enrolled in NIDCAP (M=35.96weeks, SD=5.60). Infants who enrolled within 6days of admission were discharged an average of 25days sooner (p=0.055), and at a younger post-menstrual age (by 3.33weeks on average), than those enrolled later (p=0.027).
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http://dx.doi.org/10.1016/j.pedn.2016.11.001DOI Listing
September 2017

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.

EGEMS (Wash DC) 2016 11;4(1):1244. Epub 2016 Sep 11.

National Academy of Sciences.

Objective: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is 'fit' for specific uses.

Materials And Methods: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework's inclusiveness was evaluated against ten published DQ terminologies.

Results: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.

Discussion: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.

Conclusion: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.
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http://dx.doi.org/10.13063/2327-9214.1244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051581PMC
September 2016

Characterization of Childhood Obesity and Behavioral Factors.

J Pediatr Health Care 2016 Sep-Oct;30(5):444-52. Epub 2015 Nov 21.

Introduction: Childhood obesity is a major public health threat in the United States. Recent data indicate that 34.2% of children ages 6 to 11 years are overweight or obese. The purpose of this study is to describe childhood obesity levels and identify risk behaviors in two school-based health centers in Michigan, one urban and one rural.

Methods: This study is a secondary data analysis from a multicenter comparative effectiveness trial. Multiple logistic regression was used to examine behavioral factors associated with overweight/obesity in children.

Results: In this sample (n = 105), 41.9% were obese and 16.2% were overweight. The duration of sleep per night (p = .04) and the frequency of eating breakfast (p = .04) were significant predictors of being overweight/obese.

Discussion: Health care providers in school-based health centers must be comfortable assessing, preventing, and treating childhood obesity in this high-risk group of patients. Interventions should encourage children to eat breakfast daily and to get adequate sleep.
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http://dx.doi.org/10.1016/j.pedhc.2015.10.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783778PMC
April 2018

Exploring the relationship of functional network connectivity to latent trajectories of alcohol use and risky sex.

Curr HIV Res 2014 ;12(4):293-300

Department of Psychology & Neuroscience, University of Colorado Boulder, Muenzinger D244, 345 UCB, Boulder, CO 80309-0345, USA.

Alcohol use is a major risk factor associated with unprotected sexual behavior, leading to higher risk of sexually transmitted infections (STI) including the human immunodeficiency virus (HIV). Emerging largely cross-sectional data suggest functional network connectivity strength is associated with problematic alcohol use, and as evidence supports a relationship between risky sexual behaviors and alcohol use, we hypothesized that functional connectivity might be associated with both categories of risk behavior. As part of a sexual risk reduction intervention study, juvenile justice-involved adolescents (N = 239) underwent a baseline functional magnetic resonance imaging scan and completed questionnaires about their alcohol use and risky sexual behavior at 3-month intervals over 12 months of follow up. To test both cross-sectional and longitudinal relationships between alcohol use and sexual risk behaviors, we estimated a parallel process latent growth model that simultaneously modeled the trajectories of alcohol use and sexual risk behavior. Functional connectivity strength was included as an exogenous variable to evaluate its relationship with level of risk and change in risk over time in both behaviors. Associations were found between baseline alcohol use and risky sex, and between longitudinal trajectories of alcohol use and risky sex. Network functional connectivity strength of the dorsal default mode network was associated with initial and longitudinal alcohol use, which may suggest that self-awareness of the effects of alcohol could serve as a useful target to decrease subsequent risky sexual behavior in adolescence.
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http://dx.doi.org/10.2174/1570162x12666140721124441DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759013PMC
April 2015

Associations between fractional anisotropy and problematic alcohol use in juvenile justice-involved adolescents.

Am J Drug Alcohol Abuse 2013 Nov;39(6):365-71

Department of Psychology and Neuroscience, University of Colorado Boulder , Boulder, CO , USA.

Background: Studies have shown associations between heavy alcohol use and white matter alterations in adolescence. Youth involved with the juvenile justice system engage in high levels of risk behavior generally and alcohol use in particular as compared to their non-justice-involved peers.

Objectives: This study explored white matter integrity among justice-involved adolescents. Analyses examined fractional anisotropy (FA) and mean diffusivity (MD) between adolescents with low and high levels of problematic alcohol use as assessed by the Alcohol Use Disorders Identification Test (AUDIT).

Methods: Participants (N = 125; 80% male; 14-18 years) completed measures assessing psychological status and substance use followed by diffusion tensor imaging (DTI). DTI data for low (n = 51) and high AUDIT (n = 74) adolescents were subjected to cluster-based group comparisons on skeletonized FA and MD data.

Results: Whole-brain analyses revealed significantly lower FA in clusters in the right and left posterior corona radiata (PCR) and right superior longitudinal fasciculus (SLF) in the high AUDIT group, as well as one cluster in the right anterior corona radiata that showed higher FA in the high AUDIT group. No differences in MD were identified. Exploratory analyses correlated cluster FA with measures of additional risk factors. FA in the right SLF and left PCR was negatively associated with impulsivity.

Conclusion: Justice-involved adolescents with alcohol use problems generally showed poorer FA than their low problematic alcohol use peers. Future research should aim to better understand the nature of the relationship between white matter development and alcohol use specifically as well as risk behavior more generally.
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http://dx.doi.org/10.3109/00952990.2013.834909DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4136466PMC
November 2013

The economic impact of project MARS (motivating adolescents to reduce sexual risk).

Health Psychol 2013 Sep;32(9):1003-12

Department of Economics, University of New Mexico, Albuquerque, NM 87131-0001, USA.

Objective: The purpose of this study was to economically evaluate Project MARS (Motivating Adolescents to Reduce Sexual Risk; T. J. Callahan, E. A. Montanaro, R. E. Magnan, & A. D. Bryan, 2013, "Project MARS: Design of a multi-behavior intervention trial for justice-involved youth," Translational Behavioral Medicine, Vol. 3, pp. 122-130), an ongoing, randomized, sexual-risk-reduction intervention for justice-involved youth. We consider the effect of including viral STIs in the economic analysis, and explore the impact of the MARS intervention on the perceived cost of acquiring STIs to justice-involved youth.

Method: 206 participants, ages 14 to 18, participated in a sexual-risk-reduction intervention that included screening and treatment for chlamydia and gonorrhea. A Bernoulli probability model was used to estimate averted STIs attributable to the MARS intervention. The economic benefit of averted STIs was monetized using the direct medical cost of treatment. In addition, we used a contingent valuation (willingness-to-pay) model to investigate the impact of the Project MARS on participants' perceived cost of acquiring an STI.

Results: Using the standard outcome domains typically used to evaluate STI interventions, Project MARS resulted in a reduction of $2.08 in direct medical costs for every $1 spent. When viral STIs were added to the economic model, a considerable increase in averted direct medical costs ($2.68 for every $1 spent) was found. Preliminary contingent valuation estimates suggest that participants' willingness-to-pay for averted STIs significantly increased after receiving the MARS intervention.

Conclusion: From an economic perspective, Project MARS is a worthwhile program to adopt. Future attention should be given to the impact of behavioral interventions on viral infections.
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http://dx.doi.org/10.1037/a0033607DOI Listing
September 2013

Evaluating an Integrative Theoretical Framework for HIV Sexual Risk among Juvenile Justice involved Adolescents.

J AIDS Clin Res 2013 Jun;4(6):217

University of Colorado Boulder, USA ; Center on Alcoholism, Substance Abuse and Addictions, USA.

Juvenile justice involved youth are at great risk for negative outcomes of risky sexual behavior including HIV/AIDS. Given the strong connection between alcohol use and risky sex in this population, it is important to consider alcohol use in interventions designed to decrease risky sexual behavior. This paper provides support for an integrative translational model that incorporates psychosocial, neurobiological, and genetic factors to better predict alcohol-related sexual risk behavior. Specifically, we present the design, methods, and baseline data from a complex randomized control trial, Project SHARP (Sexual Health and Adolescent Risk Prevention) in order to illustrate how this broad array of factors can best predict alcohol-related sexual risk behavior. Participants were justice-involved adolescents (n=284) who completed an fMRI and self-report assessments prior to randomization to either a sexual risk plus alcohol risk reduction group intervention or to an information-only contact control group intervention. Structural equation modeling was utilized and findings supported the hypothesized relationships in the translational model. Preliminary data suggest that interventions among justice-involved adolescents targeting alcohol-related sexual risk behavior may be more effective if a biopsychosocial approach is considered.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128495PMC
June 2013

Project MARS: Design of a Multi-Behavior Intervention Trial for Justice-Involved Youth.

Transl Behav Med 2013 Mar 24;3(1):122-130. Epub 2013 Jan 24.

University of Colorado Boulder.

Background: Marijuana and alcohol use are associated with increased sexual risk behavior among justice-involved youth. A multi-behavior intervention may reduce all three risk behaviors.

Purpose: To examine the relationships among multiple risk behaviors and the Theory of Planned Behavior (TPB) constructs guiding the development of the MARS (Motivating Adolescents to Reduce Sexual risk) intervention. We describe the MARS study design to inform the process through which a multi-behavior intervention trial can be implemented and evaluated.

Methods: Participants completed questionnaires prior to randomization to one of three interventions.

Results: Relationships were found between TPB constructs and risk behavior. A single latent variable was inadequate to capture all three risk behaviors.

Conclusions: Interventions to reduce sexual risk behavior can include content related to the role of substance use in influencing sexual risk behavior with only minimal modifications to the curriculum, and preliminary data suggest a common theory can apply across risk behaviors.
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http://dx.doi.org/10.1007/s13142-013-0192-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3583233PMC
March 2013
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