Anaesthesia monitoring using fuzzy logic.
Objective: 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 process...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 25; no. 5; pp. 339 - 348 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104350809&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104350809 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Oct2011 vid: 25 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104350809 66914606 NLM22033574 2011348174 10.1007/s10877-011-9315-z NLM22033574 104350809 ppf: 339 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Anaesthesia monitoring using fuzzy logic. aug: au: Baig MM Gholamhosseini H Kouzani A Harrison MJ Baig, Mirza Mansoor Gholamhosseini, Hamid Kouzani, Abbas Harrison, Michael J affil: School of Engineering, Auckland University of Technology, New Zealand sug: subj: Anesthesia Methods Expert Systems Logic Monitoring, Physiologic Methods Anesthesiology Trends Cluster Analysis Decision Support Techniques Medical Practice, Evidence-Based Human Blood Volume ab: Objective: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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