1
|
Tahmasebian S, Langarizadeh M, Ghazisaeidi M, Safdari R. Semantic-Web Architecture for Electronic Discharge Summary Based on OWL 2.0 Standard. Acta Inform Med 2016; 24:182-5. [PMID: 27482132 PMCID: PMC4949045 DOI: 10.5455/aim.2016.24.182-185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Accepted: 03/25/2016] [Indexed: 11/09/2022] Open
Abstract
Introduction: Patients’ electronic medical record contains all information related to treatment processes during hospitalization. One of the most important documents in this record is the record summary. In this document, summary of the whole treatment process is presented which is used for subsequent treatments and other issues pertaining to the treatment. Using suitable architecture for this document, apart from the aforementioned points we can use it in other fields such as data mining or decision making based on the cases. Material and Methods: In this study, at first, a model for patient’s medical record summary has been suggested using semantic web-based architecture. Then, based on service-oriented architecture and using Java programming language, a software solution was designed and run in a way to generate medical record summary with this structure and at the end, new uses of this structure was explained. Results: in this study a structure for medical record summaries along with corrective points within semantic web has been offered and a software running within Java along with special ontologies are provided. Discussion and Conclusion: After discussing the project with the experts of medical/health data management and medical informatics as well as clinical experts, it became clear that suggested design for medical record summary apart from covering many issues currently faced in the medical records has also many advantages including its uses in research projects, decision making based on the cases etc.
Collapse
Affiliation(s)
- Shahram Tahmasebian
- Department of Health Information Management, School of Allied Medical Sciences, Tehran, University of Medical Sciences, Tehran, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Marjan Ghazisaeidi
- Department of Health Information Management, School of Allied Medical Sciences, Tehran, University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Department of Health Information Management, School of Allied Medical Sciences, Tehran, University of Medical Sciences, Tehran, Iran
| |
Collapse
|
2
|
Shin D, Arthur G, Popescu M, Korkin D, Shyu CR. Uncovering influence links in molecular knowledge networks to streamline personalized medicine. J Biomed Inform 2014; 52:394-405. [PMID: 25150201 DOI: 10.1016/j.jbi.2014.08.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Revised: 08/04/2014] [Accepted: 08/08/2014] [Indexed: 01/10/2023]
Abstract
OBJECTIVES We developed Resource Description Framework (RDF)-induced InfluGrams (RIIG) - an informatics formalism to uncover complex relationships among biomarker proteins and biological pathways using the biomedical knowledge bases. We demonstrate an application of RIIG in morphoproteomics, a theranostic technique aimed at comprehensive analysis of protein circuitries to design effective therapeutic strategies in personalized medicine setting. METHODS RIIG uses an RDF "mashup" knowledge base that integrates publicly available pathway and protein data with ontologies. To mine for RDF-induced Influence Links, RIIG introduces notions of RDF relevancy and RDF collider, which mimic conditional independence and "explaining away" mechanism in probabilistic systems. Using these notions and constraint-based structure learning algorithms, the formalism generates the morphoproteomic diagrams, which we call InfluGrams, for further analysis by experts. RESULTS RIIG was able to recover up to 90% of predefined influence links in a simulated environment using synthetic data and outperformed a naïve Monte Carlo sampling of random links. In clinical cases of Acute Lymphoblastic Leukemia (ALL) and Mesenchymal Chondrosarcoma, a significant level of concordance between the RIIG-generated and expert-built morphoproteomic diagrams was observed. In a clinical case of Squamous Cell Carcinoma, RIIG allowed selection of alternative therapeutic targets, the validity of which was supported by a systematic literature review. We have also illustrated an ability of RIIG to discover novel influence links in the general case of the ALL. CONCLUSIONS Applications of the RIIG formalism demonstrated its potential to uncover patient-specific complex relationships among biological entities to find effective drug targets in a personalized medicine setting. We conclude that RIIG provides an effective means not only to streamline morphoproteomic studies, but also to bridge curated biomedical knowledge and causal reasoning with the clinical data in general.
Collapse
Affiliation(s)
- Dmitriy Shin
- University of Missouri, School of Medicine, Department of Pathology and Anatomical Sciences, Columbia, MO 65212, United States; University of Missouri, Graduate School, MU Informatics Institute, Columbia, MO 65211, United States.
| | - Gerald Arthur
- University of Missouri, School of Medicine, Department of Pathology and Anatomical Sciences, Columbia, MO 65212, United States; University of Missouri, Graduate School, MU Informatics Institute, Columbia, MO 65211, United States
| | - Mihail Popescu
- University of Missouri, School of Medicine, Department of Health Management and Informatics, Columbia, MO 65212, United States; University of Missouri, Graduate School, MU Informatics Institute, Columbia, MO 65211, United States; University of Missouri, College of Engineering, Department of Computer Science, Columbia, MO 65211, United States
| | - Dmitry Korkin
- Worcester Polytechnic Institute, Department of Computer Science, Department of Biology and Biotechnology, Department of Applied Math, Worcester, MA 01609, United States
| | - Chi-Ren Shyu
- University of Missouri, Graduate School, MU Informatics Institute, Columbia, MO 65211, United States; University of Missouri, College of Engineering, Department of Electrical and Computer Engineering, Columbia, MO 65211, United States
| |
Collapse
|
4
|
Barriot R, Breckpot J, Thienpont B, Brohée S, Van Vooren S, Coessens B, Tranchevent LC, Van Loo P, Gewillig M, Devriendt K, Moreau Y. Collaboratively charting the gene-to-phenotype network of human congenital heart defects. Genome Med 2010; 2:16. [PMID: 20193066 PMCID: PMC2873794 DOI: 10.1186/gm137] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2009] [Revised: 01/15/2010] [Accepted: 03/01/2010] [Indexed: 01/20/2023] Open
Abstract
Background How to efficiently integrate the daily practice of molecular biologists, geneticists, and clinicians with the emerging computational strategies from systems biology is still much of an open question. Description We built on the recent advances in Wiki-based technologies to develop a collaborative knowledge base and gene prioritization portal aimed at mapping genes and genomic regions, and untangling their relations with corresponding human phenotypes, congenital heart defects (CHDs). This portal is not only an evolving community repository of current knowledge on the genetic basis of CHDs, but also a collaborative environment for the study of candidate genes potentially implicated in CHDs - in particular by integrating recent strategies for the statistical prioritization of candidate genes. It thus serves and connects the broad community that is facing CHDs, ranging from the pediatric cardiologist and clinical geneticist to the basic investigator of cardiogenesis. Conclusions This study describes the first specialized portal to collaboratively annotate and analyze gene-phenotype networks. Of broad interest to the biological community, we argue that such portals will play a significant role in systems biology studies of numerous complex biological processes. CHDWiki is accessible at http://www.esat.kuleuven.be/~bioiuser/chdwiki
Collapse
Affiliation(s)
- Roland Barriot
- Bioinformatics Group, Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|