J Clin Monit Comput - Anaesthesia monitoring using fuzzy logic.

Tópicos

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Resumo

JECTIVE: Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration.METHOD: The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data.RESULTS: The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures.CONCLUSION: The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

Resumo Limpo

jectiv human limit abil accur continu analys larg amount data recent time rapid growth patient monitor medic data analysi use smart monitor system fuzzi logicbas expert system can mimic human thought process complex circumst indic potenti improv clinician perform accur execut repetit task human illsuit main goal studi develop clinic use diagnost alarm system base fuzzi logic detect critic event anaesthesia administrationmethod propos diagnost alarm system call fuzzi logic monitor system flms present new diagnost rule membership function mfs develop addit fuzzi infer system fis adapt neuro fuzzi infer system anfi cluster techniqu explor develop flms diagnost modul perform flms base fuzzi logic expert diagnost system valid seri offlin test train test data set select random set patient dataresult accuraci diagnos generat flms valid compar diagnost inform one provid anaesthetist patient kappaanalysi use measur level agreement anaesthetist flmss diagnos detect hypovolaemia substanti level agreement observ flms human expert anaesthetist surgic proceduresconclus diagnost alarm system flms demonstr evidencebas expert diagnost system can diagnos hypovolaemia substanti degre accuraci anaesthet patient use deliv decis support anaesthetist

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