Adpative Neuro-Fuzzy Inference System Estimation Propofol dose in the induction phase during anesthesia; case study

doi:10.5829/ije.2021.34.09c.12

Cited by 4 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12. Jamali et al 2021 ANFIS Hypnosis BIS trial [75] 13. Kumar et al 2021 FL Hypnosis HR/BP simulation [76] 14.…”

Section: Future Vision For Automated Anesthesiamentioning

confidence: 99%

A Comprehensive Review on Automated Control of Anesthesia: Recent Methods, Challenges and Future Trends

Toma

Sahib²

2023

wjps

View full text Add to dashboard Cite

The safe and personalized administration of anesthetic drugs is a significant concern in clinical practice, and automated control of anesthesia can address this issue by reducing human error, such as under- or over-dosing. This has the added benefit of allowing anesthesiologists to focus on more critical tasks and emergency management. The advantages of automated anesthesia delivery are not limited to anesthesiologists alone, as patients also benefit from the personalized and safe administration of drugs. This article offers a concise overview of the latest developments in closed-loop anesthesia delivery control systems. These systems include a range of elements such as monitoring depth of anesthesia, patient modeling, control techniques, safety systems, and clinical trial validation. Although anesthesia control has undergone significant changes over the years, a fully integrated system remains elusive. To move towards personalized patient care, it is important to assess the current technological limitations, societal considerations, and implementation hurdles, in order to identify new challenges that need to be addressed by intelligent systems. The convergence of clinical and engineering approaches facilitated by automation provides a foundation for research in the field of clinical anesthesia control. This union is crucial to guaranteeing patient safety, cost-effectiveness, and efficient performance by clinicians.

show abstract

“…12. Jamali et al 2021 ANFIS Hypnosis BIS trial [75] 13. Kumar et al 2021 FL Hypnosis HR/BP simulation [76] 14.…”

Section: Future Vision For Automated Anesthesiamentioning

confidence: 99%

A Comprehensive Review on Automated Control of Anesthesia: Recent Methods, Challenges and Future Trends

Toma

Sahib²

2023

wjps

View full text Add to dashboard Cite

show abstract

“…It is also one of the ANFIS data structures. The design of the FIS can be summarized into 3 main stages: fuzzification, inference, and defuzzification [27]. All the designed clinical linguistic variables, membership functions, and rules have been included in a Mamdani FIS [28] [29].…”

Section: Fis Implementationmentioning

confidence: 99%

Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms

Jamali¹,

Gharib²

2023

IJRCS

View full text Add to dashboard Cite

In order to minimize surgical site infections, indoor air quality in hospital operating rooms is a major concern. A wide range of literature on the relevant issue has shown that air contamination diminution can be attained by applying a more efficient set of monitoring and controlling systems that improve and optimize the indoor air status level. This paper discusses a fuzzy inference system (FIS) and the integrated model neuro-fuzzy inference system (ANFIS) focusing on the control of contamination via proper airflow distribution in an operating room, which is essential to guarantee the accuracy of the surgical procedure. A deep learning estimation approach is proposed to predict incidence in the presence of airborne contamination. The project's goal is to reduce airborne contamination to improve the surgical environment and reduce the predicted incidence during surgeries. The neuro-fuzzy deep learning model was trained with a neural network structure and tested by considering 3 important parameters that affected the air quality introducing the specialization of the system to control the model’s target. Finally, the proposed approach has been put into practice by making use of data collected by sensors placed within a real operating room in a hospital in Mashhad, Iran. The proposed model attains 97.3% and 95% validation accuracy for estimating the relative humidity and particles, respectively. The efficacy of the proposed neuro-fuzzy indicates that the system significantly lowers risk values and enhances indoor air quality.

show abstract

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

Jamali,

Gharib,

Moayyedian

et al. 2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

Adpative Neuro-Fuzzy Inference System Estimation Propofol dose in the induction phase during anesthesia; case study

Cited by 4 publications

References 17 publications

A Comprehensive Review on Automated Control of Anesthesia: Recent Methods, Challenges and Future Trends

A Comprehensive Review on Automated Control of Anesthesia: Recent Methods, Challenges and Future Trends

Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

Contact Info

Product

Resources

About