A Novel Fuzzy Pain Demand Index Derived From Patient-Controlled Analgesia for Postoperative Pain

A multilayer hierarchical structure for an intelligent analysis system is described in this paper. Four levels (patients´, measurement, Web-based, and interpreting) are used to collect massive amounts from clinical information and analyze it with both traditional and artificial intelligent methods. To support this, a novel fuzzy pain demand (FPD) index derived from the interval of each bolus of patient-controlled analgesia (PCA) is designed and documented in a large-scale clinical survey. The FPD index is modeled according to a fuzzy modeling algorithm to interpret the self-titration of the drug delivery. A total of 255 patients receiving intravenous PCA using morphine (1 mg/ml) tested this index by offline analysis from this system. We found the FPD index modeled from a fuzzy modeling algorithm to interpret the self-titration of the drug delivery can show the patients´ dynamic demand and past efforts to overcome the postoperative pain. Moreover, it could become an online system to monitor patients´ demand or intent to treat their pain so these factors could be entered into a patient´s chart along with temperature, blood pressure, pulse, and respiration rates when medical practitioners check the patients.