An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Predictive models of long COVID

BACKGROUND

The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.

METHODS

We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).

FINDINGS

LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.

INTERPRETATION

ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

FUNDING

NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

A metadata framework for computational phenotypes

With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.

KG-Hub-building and exchanging biological knowledge graphs

MOTIVATION

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.

RESULTS

Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

AVAILABILITY AND IMPLEMENTATION

https://kghub.org.

GRAPE for fast and scalable graph processing and random-walk-based embedding

Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an easy-to-use evaluation of graph-representation-learning methods, therefore also positioning GRAPE as a software resource that performs a fair comparison between methods and libraries for graph processing and embedding.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

Characterizing Patient Representations for Computational Phenotyping

Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets or all available patient data, limiting the potential for novel discovery and reducing the explainability of the resulting representations. We report on an extensive, data-driven characterization of the utility of patient representation learning methods for the purpose of CP development or automatization. We conducted ablation studies to examine the impact of patient representations, built using data from different combinations of data types and sampling windows on rare disease classification. We demonstrated that the data type and sampling window directly impact classification and clustering performance, and these results differ by rare disease group. Our results, although preliminary, exemplify the importance of and need for data-driven characterization in patient representation-based CP development pipelines.

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: a use case studying depression as a risk factor for Alzheimer's disease

BACKGROUND

Causal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to identify confounders. Uncontrolled confounding from unidentified confounders threatens validity, conditioning on intermediate variables (mediators) weakens estimates, and conditioning on common effects (colliders) induces bias. Additionally, without special treatment, erroneous conditioning on variables combining roles introduces bias. However, the vast literature is growing exponentially, making it infeasible to assimilate this knowledge. To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies. We present a use case of our approach specifying a causal model for estimating the total causal effect of depression on the risk of developing Alzheimer's disease (AD) from observational data.

METHODS

We extracted computable knowledge from a literature corpus using three machine reading systems and inferred missing knowledge using logical closure operations. Using a KG framework, we mapped the output to target terminologies and combined it with ontology-grounded resources. We translated epidemiological definitions of confounder, collider, and mediator into queries for searching the KG and summarized the roles played by the identified variables. We compared the results with output from a complementary method and published observational studies and examined a selection of confounding and combined role variables in-depth.

RESULTS

Our search identified 128 confounders, including 58 phenotypes, 47 drugs, 35 genes, 23 collider, and 16 mediator phenotypes. However, only 31 of the 58 confounder phenotypes were found to behave exclusively as confounders, while the remaining 27 phenotypes played other roles. Obstructive sleep apnea emerged as a potential novel confounder for depression and AD. Anemia exemplified a variable playing combined roles.

CONCLUSION

Our findings suggest combining machine reading and KG could augment human expertise for causal feature selection. However, the complexity of causal feature selection for depression with AD highlights the need for standardized field-specific databases of causal variables. Further work is needed to optimize KG search and transform the output for human consumption.

Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions

BACKGROUND

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research.

METHODS

We developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG.

RESULTS

The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature.

CONCLUSION

NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at https://doi.org/10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

BACKGROUND

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

METHODS

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

FINDINGS

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

INTERPRETATION

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

FUNDING

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Post-COVID Phenotypic Manifestations are Associated with New-Onset Psychiatric Disease: Findings from the NIH N3C and RECOVER Studies

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective EHR cohort study of 1,603,767 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 65 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There was a significant association between six categories and newly diagnosed anxiety, mood, and psychotic disorders, with odds ratios highest for cardiovascular (1.35, 1.27-1.42) PASC-AMs. Secondary analysis revealed that the proportions of 95 individual clinical features significantly differed between patients diagnosed with different psychiatric disorders. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

FUNDING

NCATS U24 TR002306.

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

BACKGROUND

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. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS

A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.

RESULTS

Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.

CONCLUSIONS

Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study

BACKGROUND

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. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use.

METHODS

A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis.

RESULTS

Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations.

CONCLUSIONS

Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Increased risk of psychiatric sequelae of COVID-19 is highest early in the clinical course

Background

COVID-19 has been shown to increase the risk of adverse mental health consequences. A recent electronic health record (EHR)-based observational study showed an almost two-fold increased risk of new-onset mental illness in the first 90 days following a diagnosis of acute COVID-19.

Methods

We used the National COVID Cohort Collaborative, a harmonized EHR repository with 2,965,506 COVID-19 positive patients, and compared cohorts of COVID-19 patients with comparable controls. Patients were propensity score-matched to control for confounding factors. We estimated the hazard ratio (COVID-19:control) for new-onset of mental illness for the first year following diagnosis. We additionally estimated the change in risk for new-onset mental illness between the periods of 21-120 and 121-365 days following infection.

Findings

We find a significant increase in incidence of new-onset mental disorders in the period of 21-120 days following COVID-19 (3.8%, 3.6-4.0) compared to patients with respiratory tract infections (3%, 2.8-3.2). We further show that the risk for new-onset mental illness decreases over the first year following COVID-19 diagnosis compared to other respiratory tract infections and demonstrate a reduced (non-significant) hazard ratio over the period of 121-365 days following diagnosis. Similar findings are seen for new-onset anxiety disorders but not for mood disorders.

Interpretation

Patients who have recovered from COVID-19 are at an increased risk for developing new-onset mental illness, especially anxiety disorders. This risk is most prominent in the first 120 days following infection.

Funding

National Center for Advancing Translational Sciences (NCATS).

Characterizing Long COVID: Deep Phenotype of a Complex Condition

BACKGROUND

Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

METHODS

The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19.

FUNDING

We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies.

INTERPRETATION

Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

FUNDING

U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.

A biomedically oriented automatically annotated Twitter COVID-19 dataset

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.

A Biomedically oriented automatically annotated Twitter COVID-19 Dataset

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.

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

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.

The Human Phenotype Ontology in 2021

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.

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

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.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease

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.

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

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.

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

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.

Knowledge-Based Biomedical Data Science

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.

Open Agile text mining for bioinformatics: the PubAnnotation ecosystem

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.

Applying knowledge-driven mechanistic inference to toxicogenomics

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.

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

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.

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

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.

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

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.

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

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.

Three Dimensions of Reproducibility in Natural Language Processing

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 conclusion, of a finding, and of a value. Three biomedical natural language processing papers by the authors of this paper are analyzed with respect to these dimensions.

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

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.

OWL-NETS: Transforming OWL Representations for Improved Network Inference

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.

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

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.

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

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.

Characterization of Childhood Obesity and Behavioral Factors

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.

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

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.

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

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.

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

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.

Relationships between marijuana dependence and condom use intentions and behavior among justice-involved adolescents

The current study examined the relationships among marijuana dependence, a theoretical model of condom use intentions, and subsequent condom use behavior in justice-involved adolescents. Participants completed baseline measures of prior sexual and substance use behavior. Of the original 720 participants, 649 (90.13 %) completed follow-up measures 6 months later. There were high levels of marijuana use (58.7 % met criteria for dependence) and risky sexual behavior among participants. Baseline model constructs were associated with condom use intentions, and intentions were a significant predictor of condom use at follow-up. Marijuana dependence did not significantly influence the relationships between model constructs, nor did it moderate the relationship of model constructs with subsequent condom use. Findings suggest that the theoretical model of condom use intentions is equally valid regardless of marijuana dependence status, suggesting that interventions to reduce sexual risk behavior among both marijuana dependent and non-dependent justice-involved adolescents can be appropriately based on the model.

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

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.

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

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.