Abstract
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.
