Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems

Zhang, Yifan; Tian, Yu; Zhou, Tianshu; Araki, Kenji; Li, Jingsong

doi:10.1016/j.cmpb.2015.09.020

Cited by 50 publications

(26 citation statements)

References 30 publications

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“…In another paper, [41] developed a model for semantic enhancement of KB-CDSS by using knowledge engineering technique to express the domain of knowledge and the patient data in a unified model. The architectures included four different phases: (1) knowledge acquisition, (2) knowledge representation, (3) knowledge application, and (4) knowledge evaluation.…”

Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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BackgroundKnowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.ObjectiveThe two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.MethodsWe conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.ResultsThe retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.ConclusionsThe potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

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Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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“…In the work on the CDSS, (Zhang, Tian, Zhou, Araki, & Li, 2016) proposes architecture similar to what we want to build, but their work is not done in the context of telemedicine or in the avionics context.…”

Section: Post Processing With Ontology Reasoning For In-flight Medicamentioning

confidence: 99%

Discovering frequent patterns for in-flight incidents

Sene

Kamsu-Foguem

Rumeau

2018

Cognitive Systems Research

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Objectives: In order to get a clearer idea of in-flight medical emergencies management, the application of Data mining tools can be useful to facilitate knowledge discovery from data collected by existing studies. The objective of this work is to conceptualize the construction of a Clinical Decision Support System (CDSS) in three stages corresponding to the representation levels necessary to extract knowledge from information and raw data. Method: The method can be summarized in three parts: (1) in-flight medical incident data search, (2) the validation of this data using Data mining tools, (3) the construction of the CDSS in 3 steps corresponding to the levels of knowledge representation. These three steps will be carried out using tools such as EORCA (Event Oriented Representation for Collaborative Activities) which includes action codification with regard to an ontology and event representation. Result: Data processing services provide a good structuration for information about in-flight medical incidents from which useful knowledge can be generated could improve the handling of other incidents by adapting the medical emergency equipments, for example. This structuring can be facilitated by the use of CDSS to fill in any gaps, increase coherency, and provide decision makers with a more complete picture of options that might be involved in a critical situation. Conclusion: We proposed an evolving framework facilitating the description of in-flight medical emergencies with adequate data collection and appropriate information that are required for producing interesting rules and better decisions. The data collected nourishes the organization of information, which can be improved over time by continuous integration of evidence gained from the number of incidents treated. Finally, it is proposed to strengthen requirements concerning the medical equipments available on-board, particularly in the light of knowledge resulting from the selection and approval of interesting rules.

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“…The benefits of using structured ontologies is well established. [1] Ontologies form the basis for clinical decision support systems [2], enable research [3], and are essential for interoperability. [4] However, adoption and use of structured ontologies has been slow.…”

Section: Background and Significancementioning

confidence: 99%

Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department

Greenbaum

Jernite

Halpern

et al. 2017

Preprint

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ObjectiveTo determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). Materials and MethodsWe used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patient's reason for visiting the ED. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a prospective before-and-after study design.Results A total of 279,231 patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (p<0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; p=0.0004), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p<0.0001), a 95% improvement. DiscussionWe have demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 hours to 4.8 hours.Conclusion Implementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.

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Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems

Cited by 50 publications

References 30 publications

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Discovering frequent patterns for in-flight incidents

Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department

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