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Du X, Dastmalchi F, Diller MA, Brochhausen M, Garrett TJ, Hogan WR, Lemas DJ. An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing. J Am Soc Mass Spectrom 2023; 34:2857-2863. [PMID: 37874901 DOI: 10.1021/jasms.3c00248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.
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Affiliation(s)
- Xinsong Du
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Farhad Dastmalchi
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Matthew A Diller
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205, United States
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - William R Hogan
- Data Science Institute, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
- Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
- Center for Perinatal Outcomes Research, College of Medicine, University of Florida, Gainesville, Florida 32610, United States
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2
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Zayas CE, Whorton JM, Sexton KW, Mabry CD, Dowland SC, Brochhausen M. Development and validation of the early warning system scores ontology. J Biomed Semantics 2023; 14:14. [PMID: 37730667 PMCID: PMC10510162 DOI: 10.1186/s13326-023-00296-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/09/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems. METHODS We apply the Software Development Lifecycle Framework-conceived by Winston Boyce in 1970-to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building. RESULTS We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time. CONCLUSIONS Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.
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Affiliation(s)
- Cilia E Zayas
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
| | - Justin M Whorton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Kevin W Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Department of Surgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- University of Arkansas for Medical Sciences, Institute for Digital Health & Innovation, 4301 West Markham Street, Slot 781, Little Rock, AR, 72205, USA
| | - Charles D Mabry
- Department of Surgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - S Clint Dowland
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Department of Medical Humanities and Bioethics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
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3
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Blobel B, Ruotsalainen P, Brochhausen M, Prestes E, Houghtaling MA. Designing and Managing Advanced, Intelligent and Ethical Health and Social Care Ecosystems. J Pers Med 2023; 13:1209. [PMID: 37623460 PMCID: PMC10455576 DOI: 10.3390/jpm13081209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
The ongoing transformation of health systems around the world aims at personalized, preventive, predictive, participative precision medicine, supported by technology. It considers individual health status, conditions, and genetic and genomic dispositions in personal, social, occupational, environmental and behavioral contexts. In this way, it transforms health and social care from art to science by fully understanding the pathology of diseases and turning health and social care from reactive to proactive. The challenge is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e., of multidisciplinary and dynamic systems in variable context. This enables mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc., as philosophy or cognitive sciences do. The approach requires the deployment of advanced technologies including autonomous systems and artificial intelligence. This poses important ethical and governance challenges. This paper describes the aforementioned transformation of health and social care ecosystems as well as the related challenges and solutions, resulting in a sophisticated, formal reference architecture. This reference architecture provides a system-theoretical, architecture-centric, ontology-based, policy-driven model and framework for designing and managing intelligent and ethical ecosystems in general and health ecosystems in particular.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, 93053 Regensburg, Germany
- eHealth Competence Center Bavaria, Deggendorf Institute of Technology, 94469 Deggendorf, Germany
- First Medical Faculty, Charles University of Prague, 11000 Staré Město, Czech Republic
| | - Pekka Ruotsalainen
- Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland;
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Edson Prestes
- Informatics Institute, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, Brazil;
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Kreuzthaler M, Brochhausen M, Zayas C, Blobel B, Schulz S. Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems. Front Med (Lausanne) 2023; 10:1073313. [PMID: 37007792 PMCID: PMC10050682 DOI: 10.3389/fmed.2023.1073313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/13/2023] [Indexed: 03/17/2023] Open
Abstract
This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.
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Affiliation(s)
- Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Cilia Zayas
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Bernd Blobel
- Medical Faculty, University of Regensburg, Regensburg, Germany
- eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Deggendorf, Germany
- First Medical Faculty, Charles University Prague, Prague, Czechia
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- Averbis GmbH, Freiburg, Germany
- *Correspondence: Stefan Schulz,
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5
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Du X, Dastmalchi F, Ye H, Garrett TJ, Diller MA, Liu M, Hogan WR, Brochhausen M, Lemas DJ. Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software. Metabolomics 2023; 19:11. [PMID: 36745241 DOI: 10.1007/s11306-023-01974-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Farhad Dastmalchi
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Hao Ye
- Health Science Center Libraries, University of Florida, Florida, USA
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Florida, USA
| | - Matthew A Diller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Florida, Gainesville, United States.
- Center for Perinatal Outcomes Research, University of Florida College of Medicine, Gainesville, United States.
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6
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Bona JP, Utecht J, Bost S, Brochhausen M, Prior F. The PRISM semantic cohort builder: a novel tool to search and access clinical data in TCIA imaging collections. Phys Med Biol 2023; 68:014003. [PMID: 36279873 PMCID: PMC9855624 DOI: 10.1088/1361-6560/ac9d1d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
The cancer imaging archive (TICA) receives and manages an ever-increasing quantity of clinical (non-image) data containing valuable information about subjects in imaging collections. To harmonize and integrate these data, we have first cataloged the types of information occurring across public TCIA collections. We then produced mappings for these diverse instance data using ontology-based representation patterns and transformed the data into a knowledge graph in a semantic database. This repository combined the transformed instance data with relevant background knowledge from domain ontologies. The resulting repository of semantically integrated data is a rich source of information about subjects that can be queried across imaging collections. Building on this work we have implemented and deployed a REST API and a user-facing semantic cohort builder tool. This tool allows allow researchers and other users to search and identify groups of subject-level records based on non-image data that were not queryable prior to this work. The search results produced by this interface link to images, allowing users to quickly identify and view images matching the selection criteria, as well as allowing users to export the harmonized clinical data.
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Affiliation(s)
- Jonathan P Bona
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Sarah Bost
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, United States of America
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America,
Department of Medical Humanities and Bioethics, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas, United States of America
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America,
Department of Radiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas, United States of America
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7
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Singh N, Braun N, Hogan W, Brochhausen M. Integration of Biobanking Architecture with Genomics Data: Genomics Integrated Biobanking Ontology (GIBO). Stud Health Technol Inform 2022; 295:302-303. [PMID: 35773868 DOI: 10.3233/shti220722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Integration of clinical-pathological information of Biobanks with genomics-epidemiological data/inferences in a structured and consistent manner, mitigating inherent heterogeneities of sites/sources of data/sample collection, processing, and information storage hurdles, is primary to achieving an automated surveillance system. Genomics Integrated Biobanking Ontology (GIBO) presents a solution for preserving the contextual meaning of heterogeneous data, while interlinking different genomics and epidemiological concepts in machine comprehensible format with the biobank framework. GIBO an OWL ontology introduces 84 new classes to integrate genomics data relevant to public health.
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Affiliation(s)
- Nitya Singh
- Emerging Pathogens Institute, Food Systems Institute, Animal Sciences Department, University of Florida, Gainesville, Florida, USA
| | - Naomi Braun
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - William Hogan
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Mathias Brochhausen
- Dept. of Biomedical Informatics, University of Arkansas for Medical Sciences, USA
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8
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Brochhausen M, Whorton JM, Zayas CE, Kimbrell MP, Bost SJ, Singh N, Brochhausen C, Sexton KW, Blobel B. Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective. J Pers Med 2022; 12:757. [PMID: 35629179 PMCID: PMC9147545 DOI: 10.3390/jpm12050757] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 02/05/2023] Open
Abstract
To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis. In creating surgical biobanks, currently, many obstacles must be overcome. Roadblocks exist around scoping of data that is to be collected, and the semantic integration of these data. In this paper, the generic component model and the Semantic Web technology stack are used to solve issues related to data integration. The results are twofold: (a) a scoping analysis of data and the ontologies required to harmonize and integrate it, and (b) resolution of common data integration issues in integrating data relevant to trauma surgery.
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Affiliation(s)
- Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Justin M Whorton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Cilia E Zayas
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Monica P Kimbrell
- Trauma Performance Improvement Coordinator, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Sarah J Bost
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32611, USA
| | - Nitya Singh
- Department of Animal Sciences, University of Florida, Gainesville, FL 32611, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA
| | - Christoph Brochhausen
- Central Biobank, Institute of Pathology, University and University Clinic of Regensburg, 93053 Regensburg, Germany
| | - Kevin W Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
- Institute for Digital Health & Innovation, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
- BioVentures LLC, Little Rock, AR 72205, USA
| | - Bernd Blobel
- Medical Faculty, University of Regensburg, 93053 Regensburg, Germany
- eHealth Competence Center Bavaria, Deggendorf Institute of Technology, 94469 Deggendorf, Germany
- First Medical Faculty, Charles University, 11636 Prague 1, Czech Republic
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9
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Du X, Aristizabal-Henao JJ, Garrett TJ, Brochhausen M, Hogan WR, Lemas DJ. A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research. Metabolites 2022; 12:87. [PMID: 35050209 PMCID: PMC8779534 DOI: 10.3390/metabo12010087] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/25/2021] [Accepted: 01/10/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | | | - Timothy J. Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL 32610, USA;
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - William R. Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | - Dominick J. Lemas
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
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10
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Brochhausen M, Hester DM. Data Properties or Analytical Methodologies: Too Much Attention to the Former Ignores Concerns About the Latter. Am J Bioeth 2021; 21:70-72. [PMID: 34806969 DOI: 10.1080/15265161.2021.1991037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
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11
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Blobel B, Ruotsalainen P, Brochhausen M. Autonomous Systems and Artificial Intelligence - Hype or Prerequisite for P5 Medicine? Stud Health Technol Inform 2021; 285:3-14. [PMID: 34734847 DOI: 10.3233/shti210567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and patient safety as well as more effective and efficient care processes, health and social care systems around the globe undergo an organizational, methodological and technological transformation towards personalized, preventive, predictive, participative precision medicine (P5 medicine). This paper addresses chances, challenges and risks of specific disruptive methodologies and technologies for the transformation of health and social care systems, especially focusing on the deployment of intelligent and autonomous systems.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, Germany.,eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Germany.,First Medical Faculty, Charles University Prague, Czech Republic
| | - Pekka Ruotsalainen
- Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Finland
| | - Mathias Brochhausen
- Dept. of Biomedical Informatics, University of Arkansas for Medical Sciences, USA
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12
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Vita R, Zheng J, Jackson R, Dooley D, Overton JA, Miller MA, Berrios DC, Scheuermann RH, He Y, McGinty HK, Brochhausen M, Lin AY, Jain SB, Chibucos MC, Judkins J, Giglio MG, Feng IY, Burns G, Brush MH, Peters B, Stoeckert CJ. Standardization of assay representation in the Ontology for Biomedical Investigations. Database (Oxford) 2021; 2021:6318069. [PMID: 34244718 PMCID: PMC8271124 DOI: 10.1093/database/baab040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/24/2021] [Accepted: 06/15/2021] [Indexed: 11/24/2022]
Abstract
The Ontology for Biomedical Investigations (OBI) underwent a focused review of assay term annotations, logic and hierarchy with a goal to improve and standardize these terms. As a result, inconsistencies in W3C Web Ontology Language (OWL) expressions were identified and corrected, and additionally, standardized design patterns and a formalized template to maintain them were developed. We describe here this informative and productive process to describe the specific benefits and obstacles for OBI and the universal lessons for similar projects.
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Affiliation(s)
- Randi Vita
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Jie Zheng
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Rebecca Jackson
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.,Knocean Inc, Toronto, 105 Quebec Ave, ON M2P 2T3, Canada
| | - Damion Dooley
- Centre for Infectious Disease Genomics and One Health, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
| | - James A Overton
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.,Knocean Inc, Toronto, 105 Quebec Ave, ON M2P 2T3, Canada
| | - Mark A Miller
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Daniel C Berrios
- USRA/NASA Ames Research Center, Building N-260, Moffett Field, CA 94305, USA
| | - Richard H Scheuermann
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.,Department of Informatics, J. Craig Venter Institute, 4120 Capricorn Ln, La Jolla, CA 92037, USA.,Department of Pathology, University of California, 9500 Gilman Dr, San Diego, CA 92093, USA
| | - Yongqun He
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, 1500 E Medical Center Dr, Ann Arbor, MI 48109, USA
| | - Hande Küçük McGinty
- Department of Chemistry and Biochemistry, Ohio University, 1 Ohio University Drive, Athens, OH 45701, USA
| | - Mathias Brochhausen
- Translational Research Institute, University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, USA
| | - Aisyah Yu Lin
- National Center for Ontological Research, University at Buffalo, 126 Park Hall, Buffalo, NY 14260, USA
| | - Sagar B Jain
- Department of Informatics, J. Craig Venter Institute, 4120 Capricorn Ln, La Jolla, CA 92037, USA
| | - Marcus C Chibucos
- Institute for Genome Sciences, University of Maryland School of Medicine, 655 W Baltimore St, Baltimore, MD 21201, USA
| | - John Judkins
- Department of Biology, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Michelle G Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, 655 W Baltimore St, Baltimore, MD 21201, USA
| | - Irene Y Feng
- Department of Psychology, University of Illinois Urbana-Champaign, 506 S. Wright St, Champaign, IL 61820, USA
| | - Gully Burns
- Chan Zuckerberg Initiative, 801 Jefferson Ave, Redwood City, CA 94062, USA
| | - Matthew H Brush
- Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.,Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Christian J Stoeckert
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
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13
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Hochheiser H, Jing X, Garcia EA, Ayvaz S, Sahay R, Dumontier M, Banda JM, Beyan O, Brochhausen M, Draper E, Habiel S, Hassanzadeh O, Herrero-Zazo M, Hocum B, Horn J, LeBaron B, Malone DC, Nytrø Ø, Reese T, Romagnoli K, Schneider J, Zhang L(Y, Boyce RD. A Minimal Information Model for Potential Drug-Drug Interactions. Front Pharmacol 2021; 11:608068. [PMID: 33762928 PMCID: PMC7982727 DOI: 10.3389/fphar.2020.608068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/29/2020] [Indexed: 01/22/2023] Open
Abstract
Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.
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Affiliation(s)
- Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xia Jing
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | | | - Serkan Ayvaz
- Department of Software Engineering, Bahçeşehir University, Istanbul, Turkey
| | - Ratnesh Sahay
- Clinical Data Science, AstraZeneca, Cambridge, United Kingdom
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, Netherlands
| | - Juan M. Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Oya Beyan
- Fraunhofer Institute for Applied Information Technology, RWTH Aachen University, Aachen, Germany
| | - Mathias Brochhausen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | | | - Sam Habiel
- Open Source Electronic Health Record Alliance, Washington, DC, United States
| | | | - Maria Herrero-Zazo
- The European Bioinformatics Institute, Birney Research Group, London, United Kingdom
| | - Brian Hocum
- Genelex Corporation, Seattle, WA, United States
| | - John Horn
- School of Pharmacy, University of Washington, Seattle, WA, United States
| | - Brian LeBaron
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, United States
| | - Daniel C. Malone
- Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Katrina Romagnoli
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jodi Schneider
- School of Information Science, University of Illinois, Champaign, IL, United States
| | - Louisa (Yu) Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
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14
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Hoang L, Boyce RD, Bosch N, Stottlemyer B, Brochhausen M, Schneider J. Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation. AMIA Annu Symp Proc 2021; 2020:554-563. [PMID: 33936429 PMCID: PMC8075461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.
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Affiliation(s)
- Linh Hoang
- University of Illinois at Urbana-Champaign, Champaign, IL
| | | | - Nigel Bosch
- University of Illinois at Urbana-Champaign, Champaign, IL
| | | | | | - Jodi Schneider
- University of Illinois at Urbana-Champaign, Champaign, IL
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15
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Fedorov A, Hancock M, Clunie D, Brochhausen M, Bona J, Kirby J, Freymann J, Pieper S, J W L Aerts H, Kikinis R, Prior F. DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Med Phys 2020; 47:5953-5965. [PMID: 32772385 PMCID: PMC7721965 DOI: 10.1002/mp.14445] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 07/29/2020] [Accepted: 08/04/2020] [Indexed: 01/03/2023] Open
Abstract
Purpose The dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to “nodules ≥ 3 mm”, defined as any lesion considered to be a nodule with greatest in‐plane dimension in the range 3–30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images. Acquisition and validation methods Open source tools were utilized to parse the project‐specific XML representation of LIDC‐IDRI annotations and save the result as standard DICOM objects. Validation procedures focused on establishing compliance of the resulting objects with the standard, consistency of the data between the DICOM and project‐specific representation, and evaluating interoperability with the existing tools. Data format and usage notes The dataset utilizes DICOM Segmentation objects for storing annotations of the lung nodules, and DICOM Structured Reporting objects for communicating qualitative evaluations (nine attributes) and quantitative measurements (three attributes) associated with the nodules. The total of 875 subjects contain 6859 nodule annotations. Clustering of the neighboring annotations resulted in 2651 distinct nodules. The data are available in TCIA at https://doi.org/10.7937/TCIA.2018.h7umfurq. Potential applications The standardized dataset maintains the content of the original contribution of the LIDC‐IDRI consortium, and should be helpful in developing automated tools for characterization of lung lesions and image phenotyping. In addition to those properties, the representation of the present dataset makes it more FAIR (Findable, Accessible, Interoperable, Reusable) for the research community, and enables its integration with other standardized data collections.
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Affiliation(s)
| | | | | | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Justin Kirby
- Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | - John Freymann
- Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA
| | | | | | - Ron Kikinis
- Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
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16
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Blobel B, Ruotsalainen P, Brochhausen M, Oemig F, Uribe GA. Autonomous Systems and Artificial Intelligence in Healthcare Transformation to 5P Medicine - Ethical Challenges. Stud Health Technol Inform 2020; 270:1089-1093. [PMID: 32570549 DOI: 10.3233/shti200330] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The paper introduces a structured approach to transforming healthcare towards personalized, preventive, predictive, participative precision (P5) medicine and the related organizational, methodological and technological requirements. Thereby, the deployment of autonomous systems and artificial intelligence is inevitably. The paper discusses opportunities and challenges of those technologies from a humanistic and ethical perspective. It shortly introduces the essential concepts and principles, and critically discusses some relevant projects. Finally, it offers ways for correctly representing, specifying, implementing and deploying autonomous and intelligent systems under an ethical perspective.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, Germany.,eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Germany.,First Medical Faculty, Charles University of Prague, Czech Republic
| | | | | | - Frank Oemig
- Deutsche Telekom Healthcare and Security Solutions GmbH, Bonn, Germany
| | - Gustavo A Uribe
- The European Organization for Nuclear Research, Geneva, Switzerland.,Telematics Engineering Research Group, University of Cauca, Popayán, Colombia
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17
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Brochhausen M, Ball JW, Sanddal ND, Dodd J, Braun N, Bost S, Utecht J, Winchell RJ, Sexton KW. Collecting data on organizational structures of trauma centers: the CAFE web service. Trauma Surg Acute Care Open 2020; 5:e000473. [PMID: 32789188 PMCID: PMC7394144 DOI: 10.1136/tsaco-2020-000473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/03/2020] [Accepted: 06/28/2020] [Indexed: 11/04/2022] Open
Abstract
Background During the past several decades, the American College of Surgeons has led efforts to standardize trauma care through their trauma center verification process and Trauma Quality Improvement Program. Despite these endeavors, great variability remains among trauma centers functioning at the same level. Little research has been conducted on the correlation between trauma center organizational structure and patient outcomes. We are attempting to close this knowledge gap with the Comparative Assessment Framework for Environments of Trauma Care (CAFE) project. Methods Our first action was to establish a shared terminology that we then used to build the Ontology of Organizational Structures of Trauma centers and Trauma systems (OOSTT). OOSTT underpins the web-based CAFE questionnaire that collects detailed information on the particular organizational attributes of trauma centers and trauma systems. This tool allows users to compare their organizations to an aggregate of other organizations of the same type, while collecting their data. Results In collaboration with the American College of Surgeons Committee on Trauma, we tested the system by entering data from three trauma centers and four trauma systems. We also tested retrieval of answers to competency questions. Discussion The data we gather will be made available to public health and implementation science researchers using visualizations. In the next phase of our project, we plan to link the gathered data about trauma center attributes to clinical outcomes.
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Affiliation(s)
- Mathias Brochhausen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Jane W Ball
- American College of Surgeons, Chicago, Illinois, USA
| | | | - Jimm Dodd
- American College of Surgeons, Chicago, Illinois, USA
| | - Naomi Braun
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Sarah Bost
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - Joseph Utecht
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
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Bost S, Ball JN, Sanddal ND, Dodd J, Utecht J, Winchell RJ, Brochhausen M. ACS COT participates in study to develop comparative data on trauma care organization. Bull Am Coll Surg 2020; 105:43-48. [PMID: 34531607 PMCID: PMC8442950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Sarah Bost
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida
| | - Jane N Ball
- American College of Surgeons, Chicago, Illinois
| | | | - Jimm Dodd
- American College of Surgeons Committee on Trauma, Chicago, Illinois
| | - Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Robert J Winchell
- Department of Surgery, Weill Cornell Medical College, New York, New York
| | - Mathias Brochhausen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, PO Box 100177, Gainesville, FL 32610-0177
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19
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Bona JP, Brochhausen M, Hogan WR. Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers. BMC Bioinformatics 2019; 20:708. [PMID: 31865907 PMCID: PMC6927112 DOI: 10.1186/s12859-019-3192-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Background The Drug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their biological activity created to enable comparative effectiveness and health services researchers to query National Drug Codes (NDCs) that represent products by ingredient, by molecular disposition, by therapeutic disposition, and by physiological effect (e.g., diuretic). It is based on the RxNorm drug terminology maintained by the U.S. National Library of Medicine, and on the Chemical Entities of Biological Interest ontology. Both national drug codes (NDCs) and RxNorm unique concept identifiers (RXCUIS) can undergo changes over time that can obfuscate their meaning when these identifiers occur in historic data. We present a new approach to modeling these entities within DrOn that will allow users of DrOn working with historic prescription data to more easily and correctly interpret that data. Results We have implemented a full accounting of national drug codes and RxNorm unique concept identifiers as information content entities, and of the processes involved in managing their creation and changes. This includes an OWL file that implements and defines the classes necessary to model these entities. A separate file contains an instance-level prototype in OWL that demonstrates the feasibility of this approach to representing NDCs and RXCUIs and the processes of managing them by retrieving and representing several individual NDCs, both active and inactive, and the RXCUIs to which they are connected. We also demonstrate how historic information about these identifiers in DrOn can be easily retrieved using a simple SPARQL query. Conclusions An accurate model of how these identifiers operate in reality is a valuable addition to DrOn that enhances its usefulness as a knowledge management resource for working with historic data.
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Affiliation(s)
- Jonathan P Bona
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Mathias Brochhausen
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
| | - William R Hogan
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA
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20
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Utecht J, Ball J, Bowman SM, Dodd J, Judkins J, Maxson RT, Nabaweesi R, Pradhan R, Sanddal ND, Winchell RJ, Brochhausen M. Development and Validation of a Controlled Vocabulary: An OWL Representation of Organizational Structures of Trauma Centers and Trauma Systems. Stud Health Technol Inform 2019; 264:403-407. [PMID: 31437954 PMCID: PMC7357954 DOI: 10.3233/shti190252] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In trauma care and trauma care research there exists an implementation gap regarding a consistent controlled vocabulary to describe organizational aspects of trauma centers and trauma systems. This paper describes the development and evaluation of a controlled vocabulary for trauma care organizations. We give a detailed description of the involvement of domain experts in the domain analysis workflow and the authoring of definitions and additional term descriptions. Finally, the paper details the evaluation methodology to assess the initial version of the controlled vocabulary. The results of the evaluation show that our development process yields terms most of which find approval from domain experts not involved in the development. In addition, our evaluation tools resulted in valuable domain expert input to optimize the controlled vocabulary.
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Affiliation(s)
- Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Jane Ball
- American College of Surgeons, Chicago, Illinois, USA
| | - Stephen M Bowman
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Jimm Dodd
- American College of Surgeons, Chicago, Illinois, USA
| | - John Judkins
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert T Maxson
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Rosemary Nabaweesi
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Rohit Pradhan
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | | | - Robert J Winchell
- Department of Surgery, Weill Cornell Medical College, New York, New York, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
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21
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Bona JP, Prior FW, Zozus MN, Brochhausen M. Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective. Yearb Med Inform 2019; 28:140-151. [PMID: 31419826 PMCID: PMC6697506 DOI: 10.1055/s-0039-1677912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Objectives
: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms “semantic integration" and “knowledge representation“. This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).
Methods
: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.
Results
: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.
Conclusions
: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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Affiliation(s)
| | - Fred W Prior
- University of Arkansas for Medical Sciences, Arkansas, USA
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22
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Boyce RD, Ragueneau-Majlessi I, Yu J, Tay-Sontheimer J, Kinsella C, Chou E, Brochhausen M, Judkins J, Gufford BT, Pinkleton BE, Cooney R, Paine MF, McCune JS. Developing User Personas to Aid in the Design of a User-Centered Natural Product-Drug Interaction Information Resource for Researchers. AMIA Annu Symp Proc 2018; 2018:279-287. [PMID: 30815066 PMCID: PMC6371317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Pharmacokinetic interactions between natural products and conventional drugs can adversely impact patient outcomes. These complex interactions present unique challenges that require clear communication to researchers. We are creating a public information portal to facilitate researchers' access to credible evidence about these interactions. As part of a user-centered design process, three types of intended researchers were surveyed: drug-drug interaction scientists, clinical pharmacists, and drug compendium editors. Of the 23 invited researchers, 17 completed the survey. The researchers suggested a number of specific requirements for a natural product-drug interaction information resource, including specific information about a given interaction, the potential to cause adverse effects, and the clinical importance. Results were used to develop user personas that provided the development team with a concise and memorable way to represent information needs of the three main researcher types and a common basis for communicating the design's rationale.
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Affiliation(s)
| | | | | | | | | | | | | | - John Judkins
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | | | | | - Mary F Paine
- Washington State University, Pullman and Spokane, WA
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23
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Judkins J, Tay-Sontheimer J, Boyce RD, Brochhausen M. Extending the DIDEO ontology to include entities from the natural product drug interaction domain of discourse. J Biomed Semantics 2018; 9:15. [PMID: 29743102 PMCID: PMC5944177 DOI: 10.1186/s13326-018-0183-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 04/24/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Prompted by the frequency of concomitant use of prescription drugs with natural products, and the lack of knowledge regarding the impact of pharmacokinetic-based natural product-drug interactions (PK-NPDIs), the United States National Center for Complementary and Integrative Health has established a center of excellence for PK-NPDI. The Center is creating a public database to help researchers (primarly pharmacologists and medicinal chemists) to share and access data, results, and methods from PK-NPDI studies. In order to represent the semantics of the data and foster interoperability, we are extending the Drug-Drug Interaction and Evidence Ontology (DIDEO) to include definitions for terms used by the data repository. This is feasible due to a number of similarities between pharmacokinetic drug-drug interactions and PK-NPDIs. METHODS To achieve this, we set up an iterative domain analysis in the following steps. In Step 1 PK-NPDI domain experts produce a list of terms and definitions based on data from PK-NPDI studies, in Step 2 an ontology expert creates ontologically appropriate classes and definitions from the list along with class axioms, in Step 3 there is an iterative editing process during which the domain experts and the ontology experts review, assess, and amend class labels and definitions and in Step 4 the ontology expert implements the new classes in the DIDEO development branch. This workflow often results in different labels and definitions for the new classes in DIDEO than the domain experts initially provided; the latter are preserved in DIDEO as separate annotations. RESULTS Step 1 resulted in a list of 344 terms. During Step 2 we found that 9 of these terms already existed in DIDEO, and 6 existed in other OBO Foundry ontologies. These 6 were imported into DIDEO; additional terms from multiple OBO Foundry ontologies were also imported, either to serve as superclasses for new terms in the initial list or to build axioms for these terms. At the time of writing, 7 terms have definitions ready for review (Step 2), 64 are ready for implementation (Step 3) and 112 have been pushed to DIDEO (Step 4). Step 2 also suggested that 26 terms of the original list were redundant and did not need implementation; the domain experts agreed to remove them. Step 4 resulted in many terms being added to DIDEO that help to provide an additional layer of granularity in describing experimental conditions and results, e.g. transfected cultured cells used in metabolism studies and chemical reactions used in measuring enzyme activity. These terms also were integrated into the NaPDI repository. CONCLUSION We found DIDEO to provide a sound foundation for semantic representation of PK-NPDI terms, and we have shown the novelty of the project in that DIDEO is the only ontology in which NPDI terms are formally defined.
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Affiliation(s)
- John Judkins
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
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Brochhausen M, Burgun A, Ceusters W, Hasman A, Leong TY, Musen M, Oliveira JL, Peleg M, Rector A, Schulz S. Discussion of “Biomedical Ontologies: Toward Scientific Debate”. Methods Inf Med 2018. [DOI: 10.1055/s-0038-1625243] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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25
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Brochhausen M, Bona J, Blobel B. The Role of Axiomatically-Rich Ontologies in Transforming Medical Data to Knowledge. Stud Health Technol Inform 2018; 249:38-49. [PMID: 29866954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In the biomedical domain, there exist a number of common data models (CDM) that have experienced wide uptake. However, none of these has emerged as the common model. Recently, the demand for integrating and analyzing increasingly large data sets in clinical and translational research has led to numerous efforts to harmonize existing CDMs and integrate data curated based on those models. These efforts raise the question of how to appropriately represent the semantics of data, and, furthermore, they highlight the fact that quite often different groups have greatly different definitions of 'semantics'. The question of how to formally assure that mappings between CDMs are correct is often overlooked. The answer to these challenges lies in using axiomatically-rich ontologies that allow verifying that terms refer to the same set of entities using automatic inference. This verification is only possible by building ontologies that represent the content of the scientific disciplines in accordance with the reality of the domain of the disciplines. Organizing and managing the development of numerous orthogonal domain-specific ontologies would benefit from using an Architecture Reference Model, that helps keeping the relationships consistent within each domain and ensure that appropriate inter-domain relationships are defined. This paper will explore how a strong logical representation of the scientific domain does not only foster harmonization of CDMs, but also informs and facilitates the transition from data over information to knowledge.
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Affiliation(s)
- Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock AR, USA
| | - Jonathan Bona
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock AR, USA
| | - Bernd Blobel
- Medical Faculty, University of Regensburg, Germany
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Blobel B, Brochhausen M, Ruotsalainen P. Modeling the Personal Health Ecosystem. Stud Health Technol Inform 2018; 249:3-16. [PMID: 29866950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Complex ecosystems like the pHealth one combine different domains represented by a huge variety of different actors (human beings, organizations, devices, applications, components) belonging to different policy domains, coming from different disciplines, deploying different methodologies, terminologies, and ontologies, offering different levels of knowledge, skills, and experiences, acting in different scenarios and accommodating different business cases to meet the intended business objectives. For correctly modeling such systems, a system-oriented, architecture-centric, ontology-based, policy-driven approach is inevitable, thereby following established Good Modeling Best Practices. However, most of the existing standards, specifications and tools for describing, representing, implementing and managing health (information) systems reflect the advancement of information and communication technology (ICT) represented by different evolutionary levels of data modeling. The paper presents a methodology for integrating, adopting and advancing models, standards, specifications as well as implemented systems and components on the way towards the aforementioned ultimate approach, so meeting the challenge we face when transforming health systems towards ubiquitous, personalized, predictive, preventive, participative, and cognitive health and social care.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, Germany
| | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas of Medical Sciences, Little Rock, U.S.A
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Utecht J, Brochhausen M, Judkins J, Schneider J, Boyce RD. Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation. Stud Health Technol Inform 2017; 245:960-964. [PMID: 29295242 PMCID: PMC5765984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.
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Affiliation(s)
- Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - John Judkins
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Jodi Schneider
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
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Hogan WR, Wagner MM, Brochhausen M, Levander J, Brown ST, Millett N, DePasse J, Hanna J. The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation. J Biomed Semantics 2016; 7:50. [PMID: 27538448 PMCID: PMC4989460 DOI: 10.1186/s13326-016-0092-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 08/10/2016] [Indexed: 01/03/2023] Open
Abstract
Background We developed the Apollo Structured Vocabulary (Apollo-SV)—an OWL2 ontology of phenomena in infectious disease epidemiology and population biology—as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators. Results Apollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures. In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound. Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology. Conclusion Our ontological analysis as expressed in Apollo-SV was instrumental in developing a simulator-independent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/apollo_sv.owl.
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Affiliation(s)
- William R Hogan
- University of Florida, P.O. Box 100219, 2004 Mowry Rd, Gainesville, FL, 32610-0219, USA.
| | - Michael M Wagner
- University of Pittsburgh, 5607 Baum Boulevard, Room 434, Pittsburgh, PA, 15206, USA
| | - Mathias Brochhausen
- University of Arkansas for Medical Sciences, 4301 W. Markham St. Slot #782, Little Rock, AR, 72205, USA
| | - John Levander
- University of Pittsburgh, 5607 Baum Boulevard, Room 434G, Pittsburgh, PA, 15206, USA
| | - Shawn T Brown
- Pittsburgh Supercomputing Center, 300 S. Craig St., Pittsburgh, PA, 15213, USA
| | - Nicholas Millett
- University of Pittsburgh, 5607 Baum Boulevard, Room 435 J, Pittsburgh, PA, 15206, USA
| | - Jay DePasse
- Pittsburgh Supercomputing Center, 300 S. Craig St., Pittsburgh, PA, 15213, USA
| | - Josh Hanna
- University of Florida, P.O. Box 100212, Gainesville, FL, 32610-0212, USA
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Utecht J, Judkins J, Otte JN, Colvin T, Rogers N, Rose R, Alvi M, Hicks A, Ball J, Bowman SM, Maxson RT, Nabaweesi R, Pradhan R, Sanddal ND, Tudoreanu ME, Winchell RJ, Brochhausen M. OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems. CEUR Workshop Proc 2016; 1747:http://ceur-ws.org/Vol-1747/IT504_ICBO2016.pdf. [PMID: 28217041 PMCID: PMC5312685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Organizational structures of healthcare organizations has increasingly become a focus of medical research. In the CAFÉ project we aim to provide a web-service enabling ontology-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this challenge. Evaluation and comparison of these organizations is essential. However, this research challenge is frequently compounded by the lack of a shared terminology and the lack of effective information technology solutions for assessing and comparing these organizations. In this paper we present the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological foundation to CAFÉ's web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.
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Affiliation(s)
| | - John Judkins
- University of Arkansas for Medical Science, USA
- University of Arkansas Little Rock, USA
| | | | - Terra Colvin
- Wake Forest University Comprehensive Cancer Center
| | | | - Robert Rose
- University of Arkansas for Medical Science, USA
| | - Maria Alvi
- American College of Surgeons Committee on Trauma, USA
| | | | - Jane Ball
- American College of Surgeons Committee on Trauma, USA
| | | | | | - Rosemary Nabaweesi
- University of Arkansas for Medical Science, USA
- Arkansas Children's Hospital Research Institute
| | | | | | | | - Robert J. Winchell
- American College of Surgeons Committee on Trauma, USA
- Weill Cornell Medical College, USA
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Brochhausen M, Empey PE, Schneider J, Hogan WR, Boyce RD. Adding evidence type representation to DIDEO. CEUR Workshop Proc 2016; 1747:http://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf. [PMID: 33139971 PMCID: PMC7603805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this poster we present novel development and extension of the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). We demonstrate how reasoning over this extension of DIDEO can a) automatically create a multi-level hierarchy of evidence types from descriptions of the underlying scientific observations and b) automatically subsume individual evidence items under the correct evidence type. Thus DIDEO will enable evidence items added manually by curators to be automatically categorized into a drug-drug interaction framework with precision and minimal effort from curators. As with all previous DIDEO development this extension is consistent with OBO Foundry principles.
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Affiliation(s)
- Mathias Brochhausen
- Department of Biomedical Infonnatics, University of Arkansas for Medical Sciences, Little Rock, AR USA
| | - Philip E Empey
- Department of Pharmacy and Therapeutics, University of Pittsburgh, Pittsburgh, PA USA
| | - Jodi Schneider
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA
| | - William R Hogan
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA USA
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Judkins J, Utecht J, Brochhausen M. Easy Extraction of Terms and Definitions with OWL2TL. CEUR Workshop Proc 2016; 1747:D205. [PMID: 28035214 PMCID: PMC5189984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Facilitating good communication between semantic web specialists and domain experts is necessary to efficient ontology development. This development may be hindered by the fact that domain experts tend to be unfamiliar with tools used to create and edit OWL files. This is true in particular when changes to definitions need to be reviewed as often as multiple times a day. We developed "OWL to Term List" (OWL2TL) with the goal of allowing domain experts to view the terms and definitions of an OWL file organized in a list that is updated each time the OWL file is updated. The tool is available online and currently generates a list of terms, along with additional annotation properties that are chosen by the user, in a format that allows easy copying into a spreadsheet.
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Affiliation(s)
- John Judkins
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, USA
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Hicks A, Hanna J, Welch D, Brochhausen M, Hogan WR. The ontology of medically related social entities: recent developments. J Biomed Semantics 2016; 7:47. [PMID: 27406187 PMCID: PMC4942889 DOI: 10.1186/s13326-016-0087-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 06/21/2016] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The Ontology of Medically Related Social Entities (OMRSE) was initially developed in 2011 to provide a framework for modeling demographic data in Resource Description Framework/Web Ontology Language. It is built upon the Basic Formal Ontology and conforms to Open Biomedical Ontologies Foundry's best practices. DESCRIPTION We report recent development of OMRSE which includes representations of organizations, roles, facilities, demographic data, enrollment in insurance plans, and data about socio-economic indicators. CONCLUSIONS OMRSE's coverage has been expanding in recent years to include a wide variety of classes and has been useful in several biomedical applications.
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Affiliation(s)
- Amanda Hicks
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, USA.
| | - Josh Hanna
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, USA
| | - Daniel Welch
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Science, Little Rock, AR, USA
| | - William R Hogan
- Department of Health Outcomes and Policy, University of Florida, Gainesville, FL, USA
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Brochhausen M, Zheng J, Birtwell D, Williams H, Masci AM, Ellis HJ, Stoeckert CJ. OBIB-a novel ontology for biobanking. J Biomed Semantics 2016; 7:23. [PMID: 27148435 PMCID: PMC4855778 DOI: 10.1186/s13326-016-0068-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/21/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biobanking necessitates extensive integration of data to allow data analysis and specimen sharing. Ontologies have been demonstrated to be a promising approach in fostering better semantic integration of biobank-related data. Hitherto no ontology provided the coverage needed to capture a broad spectrum of biobank user scenarios. METHODS Based in the principles laid out by the Open Biological and Biomedical Ontologies Foundry two biobanking ontologies have been developed. These two ontologies were merged using a modular approach consistent with the initial development principles. The merging was facilitated by the fact that both ontologies use the same Upper Ontology and re-use classes from a similar set of pre-existing ontologies. RESULTS Based on the two previous ontologies the Ontology for Biobanking (http://purl.obolibrary.org/obo/obib.owl) was created. Due to the fact that there was no overlap between the two source ontologies the coverage of the resulting ontology is significantly larger than of the two source ontologies. The ontology is successfully used in managing biobank information of the Penn Medicine BioBank. CONCLUSIONS Sharing development principles and Upper Ontologies facilitates subsequent merging of ontologies to achieve a broader coverage.
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Affiliation(s)
- Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #782, Little Rock, AR 72205-7199 USA
| | - Jie Zheng
- Department of Genetics, Institute for Translational Medicine and Therapeutics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - David Birtwell
- Penn Medicine BioBank, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Heather Williams
- Penn Medicine BioBank, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Anna Maria Masci
- Department of Biostatistics and Bioinformatics, Duke Medical Center, Duke University, Durnham, USA
| | - Helena Judge Ellis
- Duke Biobank, Duke Translational Research Institute, Duke University, Durnham, USA
| | - Christian J Stoeckert
- Department of Genetics, Institute for Translational Medicine and Therapeutics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Bandrowski A, Brinkman R, Brochhausen M, Brush MH, Bug B, Chibucos MC, Clancy K, Courtot M, Derom D, Dumontier M, Fan L, Fostel J, Fragoso G, Gibson F, Gonzalez-Beltran A, Haendel MA, He Y, Heiskanen M, Hernandez-Boussard T, Jensen M, Lin Y, Lister AL, Lord P, Malone J, Manduchi E, McGee M, Morrison N, Overton JA, Parkinson H, Peters B, Rocca-Serra P, Ruttenberg A, Sansone SA, Scheuermann RH, Schober D, Smith B, Soldatova LN, Stoeckert CJ, Taylor CF, Torniai C, Turner JA, Vita R, Whetzel PL, Zheng J. The Ontology for Biomedical Investigations. PLoS One 2016; 11:e0154556. [PMID: 27128319 PMCID: PMC4851331 DOI: 10.1371/journal.pone.0154556] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 04/17/2016] [Indexed: 12/18/2022] Open
Abstract
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.
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Affiliation(s)
- Anita Bandrowski
- University of California San Diego, La Jolla, California, United States of America
| | - Ryan Brinkman
- British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Mathias Brochhausen
- University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Matthew H. Brush
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Bill Bug
- Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marcus C. Chibucos
- University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California, United States of America
| | | | - Dirk Derom
- The Vrije Universiteit Brussel, Ixelles, Brussels, Belgium
| | - Michel Dumontier
- Stanford University, Stanford, California, United States of America
| | - Liju Fan
- Ontology Workshop, LLC, Columbia, Maryland, United States of America
| | - Jennifer Fostel
- National Toxicology Program, NIEHS, National Institutes of Health, Research Triangle Park, North Carolina, United States of America
| | - Gilberto Fragoso
- Center for Biomedical Informatics and Information Technology, National Institutes of Health, Rockville, Maryland, United States of America
| | - Frank Gibson
- Royal Society of Chemistry, Cambridge, Cambridgeshire, United Kingdom
| | | | - Melissa A. Haendel
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Mervi Heiskanen
- National Cancer Institute, Rockville, Maryland, United States of America
| | | | - Mark Jensen
- University at Buffalo, Buffalo, New York, United States of America
| | - Yu Lin
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | | | - Phillip Lord
- Newcastle University, Newcastle-upon-Tyne, Tyne and Wear, United Kingdom
| | - James Malone
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Elisabetta Manduchi
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Monnie McGee
- Southern Methodist University, Dallas, Texas, United States of America
| | - Norman Morrison
- The University of Manchester, Manchester, Greater Manchester, United Kingdom
| | - James A. Overton
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Helen Parkinson
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | | | - Alan Ruttenberg
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Halle, Saxony-Anhalt, Germany
| | - Barry Smith
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Chris F. Taylor
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Carlo Torniai
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Jessica A. Turner
- Georgia State University, Atlanta, Georgia, United States of America
| | - Randi Vita
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Patricia L. Whetzel
- University of California San Diego, La Jolla, California, United States of America
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Utecht J, Brochhausen M. Measuring the Usability of Triple Stores for Knowledge Management on Trauma Care Organizations. CEUR Workshop Proc 2015; 1546:241-242. [PMID: 27013932 PMCID: PMC4803025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Affiliation(s)
- Joseph Utecht
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR
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Smith B, Arabandi S, Brochhausen M, Calhoun M, Ciccarese P, Doyle S, Gibaud B, Goldberg I, Kahn CE, Overton J, Tomaszewski J, Gurcan M. Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 2015; 6:37. [PMID: 26167381 PMCID: PMC4485195 DOI: 10.4103/2153-3539.159214] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/30/2015] [Indexed: 12/24/2022] Open
Abstract
Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.
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Affiliation(s)
- Barry Smith
- Department of Philosophy, The State University of New York at Buffalo, Buffalo, NY 14260, USA
| | | | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Michael Calhoun
- Department of Health and Human Performance, Elon University, Elon, NC 27244, USA
| | - Paolo Ciccarese
- Harvard Medical School, Massachusetts General Hospital, PerkinElmer Innovation Labs, Boston, MA 02115, USA
| | - Scott Doyle
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Bernard Gibaud
- Laboratoire du Traitement du Signal et de l'Image (LTSI), Inserm Unit 1099, University of Rennes 1, Rennes, France
| | - Ilya Goldberg
- National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Charles E Kahn
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - John Tomaszewski
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
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Ayvaz S, Horn J, Hassanzadeh O, Zhu Q, Stan J, Tatonetti NP, Vilar S, Brochhausen M, Samwald M, Rastegar-Mojarad M, Dumontier M, Boyce RD. Toward a complete dataset of drug-drug interaction information from publicly available sources. J Biomed Inform 2015; 55:206-17. [PMID: 25917055 PMCID: PMC4464899 DOI: 10.1016/j.jbi.2015.04.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Revised: 03/30/2015] [Accepted: 04/15/2015] [Indexed: 10/23/2022]
Abstract
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.
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Affiliation(s)
- Serkan Ayvaz
- Department of Computer Science, Kent State University, 241 Math and Computer Science Building, Kent, OH 44242, USA.
| | - John Horn
- Department of Pharmacy, School of Pharmacy and University of Washington Medicine, Pharmacy Services, University of Washington, H375V Health Sciences Bldg, Box 357630, Seattle, WA 98195, USA.
| | - Oktie Hassanzadeh
- IBM T.J. Watson Research Center, 1101 Kitchawan Rd Route 134, P.O. Box 218, Yorktown Heights, NY 10598, USA.
| | - Qian Zhu
- Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
| | - Johann Stan
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
| | - Nicholas P Tatonetti
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, 622 West 168th St VC5, New York, NY 10032, USA.
| | - Santiago Vilar
- Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, 622 West 168th St VC5, New York, NY 10032, USA.
| | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St, #782, Little Rock, AR 72205-7199, USA.
| | - Matthias Samwald
- Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
| | - Majid Rastegar-Mojarad
- Biomedical Statistics & Informatics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford, CA 94305, USA.
| | - Richard D Boyce
- Department of Biomedical Informatics, Suite 419, 5607 Baum Blvd, Pittsburgh, PA 15206-3701, USA.
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Bian J, Xie M, Hudson TJ, Eswaran H, Brochhausen M, Hanna J, Hogan WR. CollaborationViz: interactive visual exploration of biomedical research collaboration networks. PLoS One 2014; 9:e111928. [PMID: 25405477 PMCID: PMC4236011 DOI: 10.1371/journal.pone.0111928] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 10/09/2014] [Indexed: 11/19/2022] Open
Abstract
Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization.
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Affiliation(s)
- Jiang Bian
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
- * E-mail:
| | - Mengjun Xie
- Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, United States of America
| | - Teresa J. Hudson
- Department of Psychiatry and Behavioral Sciences, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
- Department of Veterans Affairs HSR&D Center for Mental Healthcare and Outcomes Research, Central Arkansas Veterans Healthcare System, Little Rock, AR 722205, United States of America
| | - Hari Eswaran
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
- Department of Obstetrics & Gynecology Research, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Josh Hanna
- Clinical and Translational Science Informatics and Technology, University of Florida, Gainesville, FL 32610, United States of America
| | - William R. Hogan
- Department of Health Outcomes & Policy, University of Florida, Gainesville, FL 32610, United States of America
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL 32610, United States of America
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Brochhausen M, Schneider J, Malone D, Empey PE, Hogan WR, Boyce RD. Towards a foundational representation of potential drug-drug interaction knowledge. CEUR Workshop Proc 2014; 1309:16-31. [PMID: 33139970 PMCID: PMC7603806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Inadequate representation of evidence and knowledge about potential drug-drug interactions is a major factor underlying disagreements among sources of drug information that are used by clinicians. In this paper we describe the initial steps toward developing a foundational domain representation that allows tracing the evidence underlying potential drug-drug interaction knowledge. The new representation includes biological and biomedical entities represented in existing ontologies and terminologies to foster integration of data from relevant fields such as physiology, anatomy, and laboratory sciences.
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Affiliation(s)
- Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #782, Little Rock, AR, 72205-7199
| | - Jodi Schneider
- Web-instrumented man-machine interactions, communities and semantics group, INRIA Sophia Antipolis - Méditerranée, France
| | - Daniel Malone
- College of Pharmacy, University of Arizona, Tucson, AZ, USA
| | - Philip E Empey
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - William R Hogan
- Department of Health Outcomes and Policy, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh,, Pittsburgh, USA
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Ayvaz S, Zhu Q, Hochheiser H, Brochhausen M, Horn J, Dumontier M, Samwald M, Boyce RD. Drug-drug interaction data source survey and linking. AMIA Jt Summits Transl Sci Proc 2014; 2014:16. [PMID: 25717393 PMCID: PMC4333686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
As an initial step towards the goal of a common data model for potential drug-drug interactions, we surveyed the data elements provided by the publicly available sources. Our analysis found that there is very little overlap between or across publicly available resources and that the information provided is very heterogeneous.
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Affiliation(s)
| | | | | | | | - John Horn
- University of Washington, Seattle, WA
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Abstract
BACKGROUND We built the Drug Ontology (DrOn) because we required correct and consistent drug information in a format for use in semantic web applications, and no existing resource met this requirement or could be altered to meet it. One of the obstacles we faced when creating DrOn was the difficulty in reusing drug information from existing sources. The primary external source we have used at this stage in DrOn's development is RxNorm, a standard drug terminology curated by the National Library of Medicine (NLM). To build DrOn, we (1) mined data from historical releases of RxNorm and (2) mapped many RxNorm entities to Chemical Entities of Biological Interest (ChEBI) classes, pulling relevant information from ChEBI while doing so. RESULTS We built DrOn in a modular fashion to facilitate simpler extension and development of the ontology and to allow reasoning and construction to scale. Classes derived from each source are serialized in separate modules. For example, the classes in DrOn that are programmatically derived from RxNorm are stored in a separate module and subsumed by classes in a manually-curated, realist, upper-level module of DrOn with terms such as 'clinical drug role', 'tablet', 'capsule', etc. CONCLUSIONS DrOn is a modular, extensible ontology of drug products, their ingredients, and their biological activity that avoids many of the fundamental flaws found in other, similar artifacts and meets the requirements of our comparative-effectiveness research use-case.
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Affiliation(s)
- Josh Hanna
- Division of Biomedical Informatics, University of Akransas for Medical Sciences, Little Rock, AR, USA
| | - Eric Joseph
- Division of Biomedical Informatics, University of Akransas for Medical Sciences, Little Rock, AR, USA
| | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Akransas for Medical Sciences, Little Rock, AR, USA
| | - William R Hogan
- Division of Biomedical Informatics, University of Akransas for Medical Sciences, Little Rock, AR, USA
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Brochhausen M, Fransson MN, Kanaskar NV, Eriksson M, Merino-Martinez R, Hall RA, Norlin L, Kjellqvist S, Hortlund M, Topaloglu U, Hogan WR, Litton JE. Developing a semantically rich ontology for the biobank-administration domain. J Biomed Semantics 2013; 4:23. [PMID: 24103726 PMCID: PMC4021870 DOI: 10.1186/2041-1480-4-23] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 05/15/2013] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Biobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contact's affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases. RESULTS We created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S. CONCLUSIONS In conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology.
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Affiliation(s)
- Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Martin N Fransson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nitin V Kanaskar
- Department of IT Research, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Roxana Merino-Martinez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Roger A Hall
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Loreana Norlin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sanela Kjellqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Maria Hortlund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Umit Topaloglu
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - William R Hogan
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Jan-Eric Litton
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Abstract
BACKGROUND Modeling clinical processes (and their informational representation) is a prerequisite for optimally enabling and supporting high quality and safe care through information and communication technology and meaningful use of gathered information. OBJECTIVES The paper investigates existing approaches to clinical modeling, thereby systematically analyzing the underlying principles, the consistency with and the integration opportunity to other existing or emerging projects, as well as the correctness of representing the reality of health and health services. METHODS The analysis is performed using an architectural framework for modeling real-world systems. In addition, fundamental work on the representation of facts, relations, and processes in the clinical domain by ontologies is applied, thereby including the integration of advanced methodologies such as translational and system medicine. RESULTS The paper demonstrates fundamental weaknesses and different maturity as well as evolutionary potential in the approaches considered. It offers a development process starting with the business domain and its ontologies, continuing with the Reference Model-Open Distributed Processing (RM-ODP) related conceptual models in the ICT ontology space, the information and the computational view, and concluding with the implementation details represented as engineering and technology view, respectively. CONCLUSION The existing approaches reflect at different levels the clinical domain, put the main focus on different phases of the development process instead of first establishing the real business process representation and therefore enable quite differently and partially limitedly the domain experts' involvement.
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Affiliation(s)
- Bernd Blobel
- eHealth Competence Center, University Hospital Regensburg, Regensburg, Germany.
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Hogan WR, Hanna J, Joseph E, Brochhausen M. Towards a Consistent and Scientifically Accurate Drug Ontology. CEUR Workshop Proc 2013; 1060:68-73. [PMID: 27867326 PMCID: PMC5111807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Our use case for comparative effectiveness research requires an ontology of drugs that enables querying National Drug Codes (NDCs) by active ingredient, mechanism of action, physiological effect, and therapeutic class of the drug products they represent. We conducted an ontological analysis of drugs from the realist perspective, and evaluated existing drug terminology, ontology, and database artifacts from (1) the technical perspective, (2) the perspective of pharmacology and medical science (3) the perspective of description logic semantics (if they were available in Web Ontology Language or OWL), and (4) the perspective of our realism-based analysis of the domain. No existing resource was sufficient. Therefore, we built the Drug Ontology (DrOn) in OWL, which we populated with NDCs and other classes from RxNorm using only content created by the National Library of Medicine. We also built an application that uses DrOn to query for NDCs as outlined above, available at: http://ingarden.uams.edu/ingredients. The application uses an OWL-based description logic reasoner to execute end-user queries. DrOn is available at http://code.google.com/p/dr-on.
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Slaughter L, Brochhausen M, Hogan W, Nytrø Ø. The core clinical protocol ontology (C2PO): A realist ontology for representing the recommendations within clinical guidelines. Stud Health Technol Inform 2013; 192:997. [PMID: 23920771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We present an initial version of the Core Clinical Protocol Ontology (C2PO). C2PO is an application ontology being developed for the semantic markup of clinical guidelines within the Evicare project. Evicare's goals are to learn more about the actual use of guidelines in the context of clinical care and develop systems to support physicians in answering their clinical questions. The initial implementation of C2PO includes definitions for clinical guideline recommendations, and the process of recommending. We followed a realist approach to ontology design. Design methodology for C2PO, including methods for class selection, is discussed. A collection of guidelines has been manually marked-up and a demonstration system developed in which specific clinical queries will retrieve relevant ranked recommendations. C2PO forms the basis for a lightweight approach to clinical decision support that uses a text-based representation. A future objective is to expand the system to support semantic search of normative medical texts including health records, order sets, and process descriptions.
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Affiliation(s)
- Laura Slaughter
- The Interventional Centre, Oslo University Hospital, Oslo, Norway
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Blobel B, Brochhausen M, González C, Lopez DM, Oemig F. A system-theoretical, architecture-based approach to ontology management. Stud Health Technol Inform 2012; 180:1087-1089. [PMID: 22874362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Comprehensive interoperability between distributed eHealth/pHealth environments requires that the systems involved are based on a common architectural framework and share common knowledge. The paper deals with the representation of systems by related ontologies. Therefore, the architectural principles ruling the system design and the interrelations of its components also rule the design of those ontologies and their management as exemplified.
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Affiliation(s)
- Bernd Blobel
- eHealth Competence Center, University Hospital Regensburg, Germany.
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47
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Brochhausen M, Spear AD, Cocos C, Weiler G, Martín L, Anguita A, Stenzhorn H, Daskalaki E, Schera F, Schwarz U, Sfakianakis S, Kiefer S, Dörr M, Graf N, Tsiknakis M. The ACGT Master Ontology and its applications--towards an ontology-driven cancer research and management system. J Biomed Inform 2011; 44:8-25. [PMID: 20438862 PMCID: PMC5755590 DOI: 10.1016/j.jbi.2010.04.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2009] [Revised: 04/23/2010] [Accepted: 04/27/2010] [Indexed: 11/28/2022]
Abstract
OBJECTIVE This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). METHODS ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. RESULTS To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.
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Affiliation(s)
- Mathias Brochhausen
- Institute of Formal Ontology and Medical, Information Science, Saarland University, P.O. Box 15 11 50, 66041 Saarbrücken, Germany.
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Brochhausen M, Burgun A, Ceusters W, Hasman A, Leong TY, Musen M, Oliveira JL, Peleg M, Rector A, Schulz S. Discussion of "biomedical ontologies: toward scientific debate". Methods Inf Med 2011; 50:217-236. [PMID: 21566855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Affiliation(s)
- M Brochhausen
- Saarland University, Institute of Formal Ontology and Medical Information Science, POB 151150, 66041 Saarbrücken, Germany.
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Brochhausen M, Blobel B. Architectural approach for providing relations in biomedical terminologies and ontologies. Stud Health Technol Inform 2011; 169:739-743. [PMID: 21893845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The representation of multiple relations is one of the main criteria of ontologies. In formalizing both ontologies and terminologies in biomedicine relations are used to code axioms for the classes of the ontology. However, a huge number of relations represented in medical ontologies and terminologies are derived from language and formal definition is omitted. We present a strategy based on an architectural approach to facility formal analysis of relations for use in ontology systems in biomedicine and in general.
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50
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Martin L, Anguita A, Graf N, Tsiknakis M, Brochhausen M, Rüping S, Bucur A, Sfakianakis S, Sengstag T, Buffa F, Stenzhorn H. ACGT: advancing clinico-genomic trials on cancer - four years of experience. Stud Health Technol Inform 2011; 169:734-738. [PMID: 21893844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era. The project, now completed after four years of work, involved the development of both a set of methodological approaches as well as tools and services and its testing in the context of real-world clinico-genomic scenarios. This paper describes the main experiences using the ACGT platform and its tools within one such scenario and highlights the very promising results obtained.
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Affiliation(s)
- Luis Martin
- Biomedical Informatics Group, Universidad Politécnica de Madrid, Spain.
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