The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Artificial Intelligence in Medicine: Expert Systems
Artificial Intelligence in Medicine: Expert Systems
ESTDD: Expert system for thyroid diseases diagnosis
Expert Systems with Applications: An International Journal
Associative classification of mammograms using weighted rules
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
EMERGE-A Data-Driven Medical Decision Making Aid
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A new inference mechanism using bi-level weights sum method for clinical computer assisted shock diagnosis algorithm is proposed in this paper. Shock is a very emergency physiological sign in clinical medicine. It predisposes to multi-organ failure. This inference method provides completely shock trend for clinician's judgments. We use seven paths to infer the type of shock, including hypovolemic shock, cardiogenic shock, septic shock, anaphylactic shock, neurogenic shock, endocrine shock, and pulmonary shock. A knowledge base is composed of many possibility weights that are built by experienced medical expert, each path has further detail items and every item has respective weights for each shock type. Some items are then spilt into server, moderate and mild. In this study, nine patients' data are collected and analyzed. The results provide order of shock type by bi-level weights sum method. The inference results computed by this system are coinciding with diagnosis by clinician and imply other potential shock type. These results are important for clinician because the results are not unique and which corresponds with shock physiological condition. We also distribute patients' data to another six doctors for diagnostics, and for evaluating system performance. Results reveal it can provide sufficient complete information for clinician to ensure good diagnostics and treatment for patients. This system could be used either as a clinical decision support system or as educational software.