title: Fuzzy expert systems for sequential pattern recognition for patient status monitoring in operating room.
abstract: In this paper, we present several fuzzy inference systems for monitoring patient status in an operating room. The algorithms used include recursive fuzzy inference (RFIS), and non-recursive with sequential patterns as inputs. The RFIS algorithm combines current patient status data with previous output of the inference system, therefore is able to reinforce the current finding based on previous sequential system output. The results show that the RFIS system can be tuned towards higher sensitivity for more critical status, while generating smoother inference output.
authors.affiliation: GE Healthcare Inf. Technology, Wauwautosa, WI 53226, USA. joel.xue@med.ge.com
authors.forename: Joel
authors.initials: J
authors.lastname: Xue
authors.forename: Michael
authors.initials: M
authors.lastname: Krajnak
mesh_terms: D000465:Algorithms; D000777:Anesthetics / Q000009:adverse effects; D062787:Drug Overdose / Q000517:prevention & control; D004867:Equipment Design; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D009870:Operating Room Information Systems / Q000458:organization & administration*; D009873:Operating Rooms; D010363:Pattern Recognition, Automated; D011846:Radio Waves; D015203:Reproducibility of Results; D012984:Software
chemical_list: D000777:Anesthetics