Browsing by Subject "Concept Mapping"

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    Concept Mapping: A Neuro-Scientific Approach
    (2006-08-21T15:09:53Z) Baley, Charles
    Rarely if ever are the boundaries of any scholarly body of knowledge or theory integrated in an inter-disciplinary collaboration, yet there are arguably some situations where just such a symbiotic relationship is quite compelling. One such example was recognized in the remarkable similarities that exist between complex high-tech design and the logical function and design of the human brain. An even better example exists involving the processes of learning and the potential implication for collaboration between neuroscience and adult education.
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    Mapping the rules: conceptual and logical relationships in a system for pediatric clinical decision support
    (2013-10-07) Ralston, Rick K.; Odell, Jere D.; Whipple, Elizabeth C.; Liu, Gilbert C.
    The Child Health Improvement through Computer Automation (CHICA) system uses evidence-based guidelines and information collected in the clinic and stored in an electronic medical record (EMR) to inform physician and patient decision making. CHICA helps physicians to identify and select relevant screenings and also provides personalized, just-in-time information for patients. This system relies on a database of Medical Logic Modules (MLMS) written in the Arden Rules syntax. These MLMs store observations (StorObs) during the clinical encounter which trigger potential screenings and preventive health interventions for discussion with the patient or for follow up at the next visit. This poster shows how informationists worked with the CHICA team to describe the MLMs using standard vocabularies, including Medical Subject Headings (MeSH) and Logical Observation Identifiers Names and Codes (LOINC). After assigning keywords to the database of MLMs, the informationists used visualization tools to generate maps. These maps show how rules are related by logic (shared StorObs) and by concept (shared vocabulary). The CHICA team will use these maps to identify gaps in the clinical decision support database and (if needed) to develop rules which bridge related but currently isolated concepts.