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An intelligent ventilation and oxygenation management system in neonatal intensive care using fuzzy trend template fitting

Suliman Yousef Belal1,3, Azzam Fouad George Taktak2, Andy Nevill1 and Andy Spencer1

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This paper describes an open loop feedback intelligent system for neonatal intensive care management. The system provides a tool enabling the user to make the final decision to accept or reject the advice given. The system collects 18 parameters from the bedside monitor and ventilator using a Medical Information Bus (MIB) system. Comparison between the system's recommendations and seven clinical users (three doctors and four nurses) actions was made during monitoring of seven neonates with gestation age of 27–31 weeks for 124.13 h (μ = 17.7329, σ = 5.3843 h, range = 10.40–23.85 h). The validation process compared the recommendations triggered by the system with the user feedback (agree, disagree, wait). The system made 191 recommendations in total, 33 of which (17%) were for ventilation and 158 (83%) for oxygenation. The clinician agreed with the system ventilation decisions in 30 occasions (91%) and in 148 occasions for the system oxygenation decisions (94%). The overall percentage of the agreement between the system and the clinician was 93%.


PACS

87.80.-y Biophysical techniques (research methods)

87.19.U- Hemodynamics

47.63.Ec Pulmonary fluid mechanics

07.05.Mh Neural networks, fuzzy logic, artificial intelligence

Subjects

Fluid dynamics

Instrumentation and measurement

Medical physics

Biological physics

Dates

Issue 4 (August 2005)

Received 5 February 2005, accepted for publication 26 April 2005

Published 24 May 2005



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