Fuzzy control in anesthesia

AN ADVISORY SYSTEM FOR PROPOFOL ANAESTHESIA AFTER DETERMINING THE SENSITIVITY OF THE PATIENT DURING THE INDUCTION STAGE

Determining the sensitivity of the patient (SOP) during the induction stage is important because it is related to the confidence of the anaesthetist in controlling this system. Not only does this stage involve no surgical disturbances or artifacts, it is also fast and dynamic and very suitable for identifying the response of the patient to the drug. Eight patients underwent major abdominal surgery during intravenous anaesthesia and were all classified as ASA physical status category 1 or 2. A personal computer was connected via the RS232 ports for the Dinamap (i.e., Critikon, Inc., USA) and the Graseby 3400 syringe pump (i.e., Graseby Medical Limited, UK). All the trials measured patients' systolic arterial pressure (SAP) and heart rate (HR) using a Dinamap instrument. The clinical signs (i.e., sweating (SW), lacrimation (LA), and pupil response (PR)) were observed and input regularly about 5 min by the anaesthetist. A Graseby 3400 syringe pump which can pump from 0.1 to 1200 ml/hr was used to infuse propofol. Fentanyl was injected manually by syringe according to the rule-base. Also, a hierarchical fuzzy logic monitoring and rule-based control advisory system is tested in this paper to assess the determining the patient's sensitivity in the induction stage. The patient sensitivity of the elicited rule-base from anaesthetists' experience has been tested with the 8 patients during induction stage. Therefore, the good recovery time between acceptable and fast was 75 % in 8 patients. And, the mean (SD) of recovery time was 8.13 ± 5.94 min. Furthermore, the monitoring depth of anaesthesia (DOA) in terms of deciding of the confidence of depth of anaesthesia (CDOA) has also been tested with the 8 patients during maintenance stage. The mean (SD) of the mean CDOA in 8 patients was 93.55 ± 5.80 %. We conclude that determining the SOP in the induction stage is important because it is related to the confidence of the anaesthetist in controlling this advisory system. But a longer series of patients is needed to refine the rule-bases of the SOP, and to see how widely they are applicable.

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Background:

Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.

Methods:

This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.

Results:

The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.

Conclusions:

In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.

Development of a Knowledge Based System for an Intensive Care Environment Using Ontologies

In intensive care units (ICUs), clinicians must monitor patients’ vital signs and make decisions regarding the drugs they administer. The patients’ lives depend on the quality of these decisions but experts can make mistakes. Recent technological strategies and tools can decrease these errors. In this paper, the authors describe the development of a knowledge based system (KBS) to provide support to clinicians with respect to the drugs they administer to patients with cardiopathies in ICUs to stabilize them. To develop the system, knowledge from medical experts at the Meixoeiro Hospital in Vigo (Spain) has been extracted and formally represented as an ontology. As a result, a validated KBS has been obtained, which can be helpful to experts in ICUs and whose underlying knowledge can be easily shared and reused.

Norepinephrine weaning in septic shock patients by closed loop control based on fuzzy logic

Introduction

The rate of weaning of vasopressors drugs is usually an empirical choice made by the treating in critically ill patients. We applied fuzzy logic principles to modify intravenous norepinephrine (noradrenaline) infusion rates during norepinephrine infusion in septic patients in order to reduce the duration of shock.

Methods

Septic patients were randomly assigned to norepinephrine infused either at the clinician's discretion (control group) or under closed-loop control based on fuzzy logic (fuzzy group). The infusion rate changed automatically after analysis of mean arterial pressure in the fuzzy group. The primary end-point was time to cessation of norepinephrine. The secondary end-points were 28-day survival, total amount of norepinephine infused and duration of mechanical ventilation.

Results

Nineteen patients were randomly assigned to fuzzy group and 20 to control group. Weaning of norepinephrine was achieved in 18 of the 20 control patients and in all 19 fuzzy group patients. Median (interquartile range) duration of shock was significantly shorter in the fuzzy group than in the control group (28.5 [20.5 to 42] hours versus 57.5 [43.7 to 117.5] hours; P < 0.0001). There was no significant difference in duration of mechanical ventilation or survival at 28 days between the two groups. The median (interquartile range) total amount of norepinephrine infused during shock was significantly lower in the fuzzy group than in the control group (0.6 [0.2 to 1.0] μg/kg versus 1.4 [0.6 to 2.7] μg/kg; P < 0.01).

Conclusions

Our study has shown a reduction in norepinephrine weaning duration in septic patients enrolled in the fuzzy group. We attribute this reduction to fuzzy control of norepinephrine infusion.

Trial registration

Trial registration: Clinicaltrials.gov NCT00763906.

A fuzzy system for helping medical diagnosis of malformations of cortical development

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

Artificial intelligence applications in the intensive care unit

OBJECTIVE

To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES

The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION

The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION

All literature relevant to the topic was reviewed.

DATA SYNTHESIS

Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS

The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

On the use and usefulness of fuzzy sets in medical AI

Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many medical problems. Based on a literature survey on the first 30 years, we investigate the impact fuzzy set theory has had on the work in medical AI and point out what it is most appreciated for.