scholarly journals Similarity Analyzer for Semantic Interoperability of Electronic Health Records Using Artificial Intelligence (AI)

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Arjmand Naveed
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Semantic Interoperability ◽  
Healthcare Sector ◽  
Data Mapping ◽  
Mapping Data ◽  
Data Types ◽  
Health Records ◽  
Different Types ◽  
Electronic Health

The introduction of Electronic Health Records (EHR) has opened possibilities for solving interoperability issues within the healthcare sector. However, even with the introduction of EHRs, healthcare systems like hospitals and pharmacies remain isolated with no sharing of EHRs due to semantic interoperability issues. This paper extends our previous work in which we proposed a framework that dealt with semantic interoperability and security of EHR. The extension is the proposal of a cloud-based similarity analyzer for data structuring, data mapping, data modeling and conflict removal using Word2vec Artificial Intelligence (AI) technique.  Different types of conflicts are removed from data in order to model data into common data types which can be interpreted by different stakeholders.

Research and application of artificial intelligence based on electronic health record data from patients with cancer: a systematic review (Preprint)

2021 ◽  
Author(s):  
Xinyu Yang ◽  
Dongmei Mu ◽  
Hao Peng ◽  
Hua Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Ensemble Methods ◽  
Future Research ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Patients With Cancer ◽  
Cancer Management ◽  
Treatment And Prevention ◽  
Electronic Health

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

scholarly journals Towards Semantic Interoperability for Electronic Health Records

2007 ◽  
Vol 46 (03) ◽  
pp. 332-343 ◽  
Author(s):  
P. Knaup ◽  
E. J. S. Hovenga ◽  
S. Heard ◽  
S. Garde
Keyword(s):  
Electronic Health Records ◽  
Domain Knowledge ◽  
Semantic Interoperability ◽  
Health Records ◽  
Face To Face ◽  
Telephone Interviews ◽  
Development Organisation ◽  
Knowledge Governance ◽  
Electronic Health ◽  
Definition Of

Summary Objectives: In the field of open electronic health records (EHRs), openEHR as an archetype-based approach is being increasingly recognised. It is the objective of this paper to shortly describe this approach, and to analyse how openEHR archetypes impact on health professionals and semantic interoperability. Methods: Analysis of current approaches to EHR systems, terminology and standards developments. In addition to literature reviews, we organised face-to-face and additional telephone interviews and tele-conferences with members of relevant organisations and committees. Results: The openEHR archetypes approach enables syntactic interoperability and semantic interpretability – both important prerequisites for semantic interoperability. Archetypes enable the formal definition of clinical content by clinicians. To enable comprehensive semantic interoperability, the development and maintenance of archetypes needs to be coordinated internationally and across health professions. Domain knowledge governance comprises a set of processes that enable the creation, development, organisation, sharing, dissemination, use and continuous maintenance of archetypes. It needs to be supported by information technology. Conclusions: To enable EHRs, semantic interoperability is essential. The openEHR archetypes approach enables syntactic interoperability and semantic interpretability. However, without coordinated archetype development and maintenance, ‘rank growth’ of archetypes would jeopardize semantic interoperability. We therefore believe that openEHR archetypes and domain knowledge governance together create the knowledge environment required to adopt EHRs.

scholarly journals case study in distributed team science in research using electronic health records

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Jiao Song ◽  
Elizabeth Elliot ◽  
Andrew D Morris ◽  
Joannes J Kerssens ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Oral Anticoagulants ◽  
Cost Effective ◽  
Study Cohort ◽  
Team Science ◽  
Data Types ◽  
Health Records ◽  
Distributed Team ◽  
Electronic Health ◽  
The Uk

IntroductionDue to various regulatory barriers, it is increasingly difficult to move pseudonymised routine health data across platforms and among jurisdictions. To tackle this challenge, we summarized five approaches considered to support a scientific research project focused on the risk of the new non-vitamin K Target Specific Oral Anticoagulants (TSOACs) and collaborated between the Farr institute in Wales and Scotland. ApproachIn Wales, routinely collected health records held in the Secure Anonymous Information Linkage (SAIL) Databank were used to identify the study cohort. In Scotland, data was extracted from national dataset resources administered by the eData Research & Innovation Service (eDRIS) and stored in the Scottish National Data Safe Haven. We adopted a federated data and multiple analysts approach, but arranged simultaneous accesses for Welsh and Scottish analysts to generate study cohorts separately by implementing the same algorithm. Our study cohort across two countries was boosted to 6,829 patients towards risk analysis. Source datasets and data types applied to generate cohorts were reviewed and compared by analysts based on both sites to ensure the consistency and harmonised output.  DiscussionThis project used a fusion of two approaches among five considered. The approach we adopted is a simple, yet efficient and cost-effective method to ensure consistency in analysis and coherence with multiple governance systems. It has limitations and potentials of extending and scaling. It can also be considered as an initialisation of a developing infrastructure to support a distributed team science approach to research using Electronic Health Records (EHRs) across the UK and more widely. KeywordsTeam science, cross-jurisdictional data linkage, electronic health records

Pre-Processing Highly Sparse and Frequently Evolving Standardized Electronic Health Records for Mining

2021 ◽  
pp. 8-21
Author(s):  
Shivani Batra ◽  
Shelly Sachdeva
Keyword(s):  
Electronic Health Records ◽  
Data Model ◽  
Semantic Interoperability ◽  
Relational Model ◽  
Health Records ◽  
Related Information ◽  
Health Related ◽  
Electronic Health ◽  
Mining Tools ◽  
Attribute Value

EHRs aid in maintaining longitudinal (lifelong) health records constituting a multitude of representations in order to make health related information accessible. However, storing EHRs data is non-trivial due to the issues of semantic interoperability, sparseness, and frequent evolution. Standard-based EHRs are recommended to attain semantic interoperability. However, standard-based EHRs possess challenges (in terms of sparseness and frequent evolution) that need to be handled through a suitable data model. The traditional RDBMS is not well-suited for standardized EHRs (due to sparseness and frequent evolution). Thus, modifications to the existing relational model is required. One such widely adopted data model for EHRs is entity attribute value (EAV) model. However, EAV representation is not compatible with mining tools available in the market. To style the representation of EAV, as per the requirement of mining tools, pivoting is required. The chapter explains the architecture to organize EAV for the purpose of preparing the dataset for use by existing mining tools.

scholarly journals Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

2018 ◽  
Vol 4 ◽  
pp. 205520761880465 ◽  
Author(s):  
Tim Robbins ◽  
Sarah N Lim Choi Keung ◽  
Sailesh Sankar ◽  
Harpal Randeva ◽  
Theodoros N Arvanitis
Keyword(s):  
Electronic Health Records ◽  
Missing Values ◽  
Research Output ◽  
Healthcare Research ◽  
Healthcare Sector ◽  
Diabetes Research ◽  
Health Records ◽  
Diverse Range ◽  
Secondary Use ◽  
Electronic Health

Introduction Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus. Methods Narrative literature review of articles, published in both the medical- and engineering-based informatics literature. Results There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results. Discussion Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

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