Archetype-Based Semantic Integration and Standardization of Clinical Data

Moner, David; Maldonado, José Alberto; Boscá, Diego; Fernandez, Jesualdo T.; Angulo, Carlos; Crespo, Pere; Vivancos, Pedro J.; Robles, Montserrat

doi:10.1109/iembs.2006.260124

Cited by 15 publications

(2 citation statements)

References 5 publications

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“…In the technical realm, harmonization difficulties persist. Biomedical data are usually distributed among several heterogeneous and semantically incompatible health information systems, leading to interoperability problems [ 27 ]. The goal of data integration is to create a unique semantic reference to ensure data consistency and reuse and, consequently, improve clinical practice, medical research, and personalized medicine [ 28 ].…”

Section: Barriers To Accessmentioning

confidence: 99%

Sharing Biomedical Data: Strengthening AI Development in Healthcare

et al. 2021

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Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

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Section: Barriers To Accessmentioning

confidence: 99%

Sharing Biomedical Data: Strengthening AI Development in Healthcare

et al. 2021

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“…The RM is an object-oriented model used to represent the generic and stable properties of health record information. It contains a set of primitive types, a set of classes that define the building blocks of EHRs, and a set of auxiliary classes that describe the context information to be attached to an EHR annotation (Moner et al 2006).…”

Section: The Health Archetype Conceptmentioning

confidence: 99%

Archetype-Based Electronic Health Records: A Literature Review and Evaluation of Their Applicability to Health Data Interoperability and Access

Wollersheim

Sari

Rahayu

2009

HIM J

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Health Information Managers (HIMs) are responsible for overseeing health information. The change management necessary during the transition to electronic health records (EHR) is substantial, and ongoing. Archetype-based EHRs are a core health information system component which solve many of the problems that arise during this period of change. Archetypes are models of clinical content, and they have many beneficial properties. They are interoperable, both between settings and through time. They are more amenable to change than conventional paradigms, and their design is congruent with clinical practice. This paper is an overview of the current archetype literature relevant to Health Information Managers. The literature was sourced in the English language sections of ScienceDirect, IEEE Explore, Pubmed, Google Scholar, ACM Digital library and other databases on the usage of archetypes for electronic health record storage, looking at the current areas of archetype research, appropriate usage, and future research. We also used reference lists from the cited papers, papers referenced by the openEHR website, and the recommendations from experts in the area. Criteria for inclusion were (a) if studies covered archetype research and (b) were either studies of archetype use, archetype system design, or archetype effectiveness. The 47 papers included show a wide and increasing worldwide archetype usage, in a variety of medical domains. Most of the papers noted that archetypes are an appropriate solution for future-proof and interoperable medical data storage. We conclude that archetypes are a suitable solution for the complex problem of electronic health record storage and interoperability.

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Ontology-Based Decision Support Systems for Health Data Management to Support Collaboration in Ambient Assisted Living and Work Reintegration

Spoladore¹

2017

Collaboration in a Data-Rich World

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Archetype-Based Semantic Integration and Standardization of Clinical Data

Cited by 15 publications

References 5 publications

Sharing Biomedical Data: Strengthening AI Development in Healthcare

Sharing Biomedical Data: Strengthening AI Development in Healthcare

Archetype-Based Electronic Health Records: A Literature Review and Evaluation of Their Applicability to Health Data Interoperability and Access

Ontology-Based Decision Support Systems for Health Data Management to Support Collaboration in Ambient Assisted Living and Work Reintegration

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