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  1. Article ; Online: Open source and reproducible and inexpensive infrastructure for data challenges and education

    Peter E. DeWitt / Margaret A. Rebull / Tellen D. Bennett

    Scientific Data, Vol 11, Iss 1, Pp 1-

    2024  Volume 8

    Abstract: Abstract Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure ...

    Abstract Abstract Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.
    Keywords Science ; Q
    Subject code 020
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A machine learning-based phenotype for long COVID in children

    Vitaly Lorman / Hanieh Razzaghi / Xing Song / Keith Morse / Levon Utidjian / Andrea J Allen / Suchitra Rao / Colin Rogerson / Tellen D Bennett / Hiroki Morizono / Daniel Eckrich / Ravi Jhaveri / Yungui Huang / Daksha Ranade / Nathan Pajor / Grace M Lee / Christopher B Forrest / L Charles Bailey

    PLoS ONE, Vol 18, Iss 8, p e

    An EHR-based study from the RECOVER program.

    2023  Volume 0289774

    Abstract: As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 ( ... ...

    Abstract As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.
    Keywords Medicine ; R ; Science ; Q
    Subject code 610
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Clinical decision support tools for paediatric sepsis in resource-poor settings

    Niranjan Kissoon / Matthew O Wiens / Luregn J Schlapbach / Olive Kabajaasi / Juan Camilo Jaramillo-Bustamante / Andrea Jimenez-Zambrano / Carly Ritger / Margaret Rebull / Andrew C Argent / Lauren R Sorce / R Scott Watson / Brooke Dorsey Holliman / Lazaro N Sanchez-Pinto / Tellen D Bennett

    BMJ Open, Vol 13, Iss

    an international qualitative study

    2023  Volume 10

    Abstract: Objective New paediatric sepsis criteria are being developed by an international task force. However, it remains unknown what type of clinical decision support (CDS) tools will be most useful for dissemination of those criteria in resource-poor settings. ...

    Abstract Objective New paediatric sepsis criteria are being developed by an international task force. However, it remains unknown what type of clinical decision support (CDS) tools will be most useful for dissemination of those criteria in resource-poor settings. We sought to design effective CDS tools by identifying the paediatric sepsis-related decisional needs of multidisciplinary clinicians and health system administrators in resource-poor settings.Design Semistructured qualitative focus groups and interviews with 35 clinicians (8 nurses, 27 physicians) and 5 administrators at health systems that regularly provide care for children with sepsis, April–May 2022.Setting Health systems in Africa, Asia and Latin America, where sepsis has a large impact on child health and healthcare resources may be limited.Participants Participants had a mean age of 45 years, a mean of 15 years of experience, and were 45% female.Results Emergent themes were related to the decisional needs of clinicians caring for children with sepsis and to the needs of health system administrators as they make decisions about which CDS tools to implement. Themes included variation across regions and institutions in infectious aetiologies of sepsis and available clinical resources, the need for CDS tools to be flexible and customisable in order for implementation to be successful, and proposed features and format of an ideal paediatric sepsis CDS tool.Conclusion Findings from this study will directly contribute to the design and implementation of CDS tools to increase the uptake and impact of the new paediatric sepsis criteria in resource-poor settings.
    Keywords Medicine ; R
    Subject code 360
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Risk factors associated with post-acute sequelae of SARS-CoV-2

    Elaine L. Hill / Hemalkumar B. Mehta / Suchetha Sharma / Klint Mane / Sharad Kumar Singh / Catherine Xie / Emily Cathey / Johanna Loomba / Seth Russell / Heidi Spratt / Peter E. DeWitt / Nariman Ammar / Charisse Madlock-Brown / Donald Brown / Julie A. McMurry / Christopher G. Chute / Melissa A. Haendel / Richard Moffitt / Emily R. Pfaff /
    Tellen D. Bennett / on behalf of the N3C Consortium / and the RECOVER Consortium

    BMC Public Health, Vol 23, Iss 1, Pp 1-

    an N3C and NIH RECOVER study

    2023  Volume 13

    Abstract: Abstract Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. Methods This was a ... ...

    Abstract Abstract Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. Methods This was a retrospective case–control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33–1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05–4.73), long (8–30 days, OR 1.69, 95% CI 1.31–2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45–4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18–1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40–1.60), chronic lung disease (OR 1.63, 95% CI 1.53–1.74), and obesity (OR 1.23, 95% CI 1.16–1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an ...
    Keywords Post-acute sequelae of SARS-CoV-2 ; PASC ; Long-COVID ; COVID-19 ; Risk factors ; Public aspects of medicine ; RA1-1270
    Subject code 610
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Ontologizing health systems data at scale

    Tiffany J. Callahan / Adrianne L. Stefanski / Jordan M. Wyrwa / Chenjie Zeng / Anna Ostropolets / Juan M. Banda / William A. Baumgartner / Richard D. Boyce / Elena Casiraghi / Ben D. Coleman / Janine H. Collins / Sara J. Deakyne Davies / James A. Feinstein / Asiyah Y. Lin / Blake Martin / Nicolas A. Matentzoglu / Daniella Meeker / Justin Reese / Jessica Sinclair /
    Sanya B. Taneja / Katy E. Trinkley / Nicole A. Vasilevsky / Andrew E. Williams / Xingmin A. Zhang / Joshua C. Denny / Patrick B. Ryan / George Hripcsak / Tellen D. Bennett / Melissa A. Haendel / Peter N. Robinson / Lawrence E. Hunter / Michael G. Kahn

    npj Digital Medicine, Vol 6, Iss 1, Pp 1-

    making translational discovery a reality

    2023  Volume 18

    Abstract: Abstract 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 ... ...

    Abstract Abstract 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.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 006
    Language English
    Publishing date 2023-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

    Xingmin Aaron Zhang / Amy Yates / Nicole Vasilevsky / J. P. Gourdine / Tiffany J. Callahan / Leigh C. Carmody / Daniel Danis / Marcin P. Joachimiak / Vida Ravanmehr / Emily R. Pfaff / James Champion / Kimberly Robasky / Hao Xu / Karamarie Fecho / Nephi A. Walton / Richard L. Zhu / Justin Ramsdill / Christopher J. Mungall / Sebastian Köhler /
    Melissa A. Haendel / Clement J. McDonald / Daniel J. Vreeman / David B. Peden / Tellen D. Bennett / James A. Feinstein / Blake Martin / Adrianne L. Stefanski / Lawrence E. Hunter / Christopher G. Chute / Peter N. Robinson

    npj Digital Medicine, Vol 2, Iss 1, Pp 1-

    2019  Volume 9

    Abstract: Abstract 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 ...

    Abstract Abstract 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.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Characterizing Long COVID

    Rachel R Deer / Madeline A Rock / Nicole Vasilevsky / Leigh Carmody / Halie Rando / Alfred J Anzalone / Marc D Basson / Tellen D Bennett / Timothy Bergquist / Eilis A Boudreau / Carolyn T Bramante / James Brian Byrd / Tiffany J Callahan / Lauren E Chan / Haitao Chu / Christopher G Chute / Ben D Coleman / Hannah E Davis / Joel Gagnier /
    Casey S Greene / William B Hillegass / Ramakanth Kavuluru / Wesley D Kimble / Farrukh M Koraishy / Sebastian Köhler / Chen Liang / Feifan Liu / Hongfang Liu / Vithal Madhira / Charisse R Madlock-Brown / Nicolas Matentzoglu / Diego R Mazzotti / Julie A McMurry / Douglas S McNair / Richard A Moffitt / Teshamae S Monteith / Ann M Parker / Mallory A Perry / Emily Pfaff / Justin T Reese / Joel Saltz / Robert A Schuff / Anthony E Solomonides / Julian Solway / Heidi Spratt / Gary S Stein / Anupam A Sule / Umit Topaloglu / George D. Vavougios / Liwei Wang

    EBioMedicine, Vol 74, Iss , Pp 103722- (2021)

    Deep Phenotype of a Complex Condition

    2021  

    Abstract: ABSTRACT: 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 ...

    Abstract ABSTRACT: 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. ...
    Keywords COVID-19 ; of post-acute sequelae of SARS-CoV-2 ; human phenotype ontology ; long COVID ; phenotyping ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 610
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A Framework for Future National Pediatric Pandemic Respiratory Disease Severity Triage

    Timothy Bergquist / Marie Wax / Tellen D. Bennett / Richard Moffitt / Jifan Gao / Guanhua Chen / Amalio Telenti / M. Cyrus Maher / Istvan Bartha / Lorne Walker / Benjamin Orwoll / Meenakshi Mishra / Joy Alamgir / Bruce Cragin / Pediatric COVID-19 Challenge Consortium / Christopher Ferguson / Hui Hsing Wong / Anne Deslattes Mays / Leonie Misquitta /
    Kerrie DeMarco / Kimberly Sciarretta / Sandeep Patel

    Journal of Clinical and Translational Science, Pp 1-29

    The HHS Pediatric COVID-19 Data Challenge

    Keywords Medicine ; R
    Language English
    Publisher Cambridge University Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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