The objective of this work concerns the study of virtual biosensors for the estimation of medical precursors. The principle is based on the combination of the signals coming from the patient (vital functions), the transduction of such acquired signals and the processing of the obtained information. The method will use n input variables (the classic physiological parameters and/or signals detected by using additive sensors) and one output variable which is correlated with the clinical condition of the patient. A model will produce an association between the input variables and the output variable by using a data set established with the medical team. The proposed methodology improves standard systems such as 'track and trigger' and threshold (Early Warning Score) through the adoption of the Fuzzy Set Theory.
Virtual biosensors for the estimation of medical precursors
Baglio S.;Cammarata A.;Lo Bello L.;Maddio P. D.;Patti G.;Sinatra R.;Trigona C.
2019-01-01
Abstract
The objective of this work concerns the study of virtual biosensors for the estimation of medical precursors. The principle is based on the combination of the signals coming from the patient (vital functions), the transduction of such acquired signals and the processing of the obtained information. The method will use n input variables (the classic physiological parameters and/or signals detected by using additive sensors) and one output variable which is correlated with the clinical condition of the patient. A model will produce an association between the input variables and the output variable by using a data set established with the medical team. The proposed methodology improves standard systems such as 'track and trigger' and threshold (Early Warning Score) through the adoption of the Fuzzy Set Theory.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
