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1.  A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge 
Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers.
PMCID: PMC3099486  PMID: 21611137
Medical informatics; clinical guidelines; decision support systems; knowledge representation; knowledge acquisition; knowledge bases; ontologies; information retrieval; human computer interaction; artificial intelligence; digital libraries; service oriented architecture.
2.  A Framework for Intelligent Visualization of Multiple Time-Oriented Medical Records 
Management of patients, especially chronic patients, requires presentation and processing of very large amounts of time-oriented clinical data. Using regular means such as text or tables is often ineffective, thus we propose to use the visual presentation of the information in decision support, especially in the medical domain. Displaying only raw data is not sufficient, because it still requires the user to derive meaningful conclusions from large amount of data. In order to support the computation process, we provide automated mechanisms for temporal abstraction. These mechanisms perform derivation of context-specific, interval-based abstract concepts from raw time-stamped clinical data, by using a domain-specific knowledge base. Then, these abstractions can be visualized and explored. In addition, in many cases (e.g. when comparing the effect of new drugs on various groups of patients) a view of multiple records is more effective than a view of each individual record separately. We have designed and implemented a system called VISITORS (VisualizatIon of Time-Oriented RecordS) which includes several tools for intelligent visualization and exploration of raw data and abstracted concepts for multiple patient records.
PMCID: PMC1560450  PMID: 16779071

Results 1-2 (2)