ABSTRACT
The setting and adjustment of ventilator parameters need to rely on a large amount of clinical data and rich experience. This paper explored the problem of difficult decision-making of ventilator parameters due to the time-varying and sudden changes of clinical patient's state, and proposed an expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment based on fuzzy control rule and neural network. Based on the method and the real-time physiological state of clinical patients, we generated a mechanical ventilation decision-making solution set with continuity and smoothness, and automatically provided explicit parameter adjustment suggestions to medical personnel. This method can solve the problems of low control precision and poor dynamic quality of the ventilator's stepwise adjustment, handle multi-input control decision problems more rationally, and improve ventilation comfort for patients.
Subject(s)
Humans , Ventilators, Mechanical , Respiration, Artificial , Neural Networks, ComputerABSTRACT
Objective@#To explore the night sleep quality of shift nurses and the current situation of their daytime tiredness, sleepiness, and to provide evidence for nursing administrators and managers to allocate human resources reasonably and prevent adverse events.@*Methods@#The cross-sectional method was utilized to conduct a questionnaire survey among shift nurses in a tertiary teaching hospital in Shandong Province from March to May inclusive, 2017.@*Results@#There was a total of 233 valid questionnaires returned. The prevalence of sleep disorder, daytime tiredness and sleepiness was 45.92%, 16.31% and 13.30%, respectively. The differences of the nurses' sleep quality at night between different ages, marriages, educational backgrounds and professional titles were statistically significant (P<0.05) , while the differences of daytime burnout and sleep state between different shift systems were statistically significant (P<0.01) . Night sleep quality was positively correlated with daytime tiredness and sleepiness (P<0.05) . The results of multiple linear regression analysis showed that age, marriage, educational background and professional title had an impact on nurses' sleep quality at night (P<0.05) . Shift system had an impact on nurses' daytime burnout and sleep apnea (P<0.01) .@*Conclusion@#There was a high prevalence of night sleep disorder, daytime tiredness and sleepiness among the shift nurses. Nursing administrators and managers should pay more attention to the night sleep quality of nurses who aged over 30 years old, married, without a bachelor degree and those with a lower professional rank. Furthermore, the current situation of daytime tiredness and sleepiness among two-shift only nurses was worrisome.