Creating personalised clinical pathways by semantic interoperability with electronic health records

Wang, Hua-Qiong; Li, Jingsong; Zhang, Yifan; Suzuki, Masato; Araki, Kenji

doi:10.1016/j.artmed.2013.02.005

Cited by 50 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The semantic web provides a common framework that allows data to be shared and reused across applications, enterprises and community boundaries [30], and receives widely adopted in healthcare data integration [31–33]. Moreover, existing standards such as HL7 [34], SNOMED CT [9], and ICD 9/10 have been established to normalize the conceptual model of health data [35]. Hence, we adopt semantic technologies to achieve the integration of health data with medical knowledge.…”

Section: Healthcare Information Organization Modelmentioning

confidence: 99%

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

Shi

Shijian

Yang

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.

show abstract

Section: Healthcare Information Organization Modelmentioning

confidence: 99%

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

Shi

Shijian

Yang

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Eventually, the hope is for a CDSS that includes learning algorithms to make recommendations based on previous successful treatments [3]. Some prototype systems are in development [3,[90][91][92].…”

Section: Clinical Decision Supportmentioning

confidence: 99%

“Big Data” and the Electronic Health Record

Ross¹,

Wei²,

Ohno-Machado³

2014

Yearb Med Inform

136

View full text Add to dashboard Cite

SummaryObjectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods: We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

show abstract

“…In other cases, data are available in XML format but can be readily transformed into RDF using custom ontologies as it was done in [37]. There have been a number of works showing the benefits of Semantic Web technologies to translational science and personalized medicine [38][39][40][41][42][43][44][45][46][47][48] such as, for instance, logical language of RDF/N3 extension [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Uncovering influence links in molecular knowledge networks to streamline personalized medicine

Shin

Arthur

Popescu

et al. 2014

Journal of Biomedical Informatics

View full text Add to dashboard Cite

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.

show abstract

Creating personalised clinical pathways by semantic interoperability with electronic health records

Cited by 50 publications

References 27 publications

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

“Big Data” and the Electronic Health Record

Uncovering influence links in molecular knowledge networks to streamline personalized medicine

Contact Info

Product

Resources

About