Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: The case of a gas refinery

Lower back pain (LBP) is a common medical problem that deprives many individuals of their normal lifestyles and keeps them from routine activities. Diagnosing LBP is challenging because it requires highly specialized knowledge involving a complex anatomical and physiological structure as well as diverse clinical considerations. Although a handful of studies have proposed or developed systems to support LBP diagnosis and improve knowledge sharing, these systems have limited scope, lack systematic evaluations, and/or ignore diagnoses that consist of multiple parts (i.e., decision outcomes), each of which corresponds to a particular medical condition, disease, or abnormality. In this study, we design, implement, and evaluate a Web-based decision support system that employs an intuitive and easy-to-use framework to assess the patient's information and recommend a diagnosis consisting of one or multiple parts. Our system design addresses the challenging characteristics of a LBP diagnosis and uses verbal probability estimation to represent and reason about the associated uncertainty. Our evaluations are systematic, including knowledge base verification, system validation using a modified Turing test, and clinical efficacy assessment involving 5 clinicians and 180 real-world cases collected from geographically dispersed clinics. Our evaluation design is more thorough than those used by most previous studies, and the proposed system is relatively ready for clinical deployment. Therefore, this study both contributes to decision support systems research and has advanced clinical support for LBP diagnosis. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.

A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations

A new method for sleep apnea classification using wavelets and feedforward neural networks

The sleep apnea/hypopnea syndrome is a very common sleep disorder, characterised by disrupted breathing during sleep. Depending on the extent of the disruptions to sleep, these are classified as apneas or hypopneas. In order to locate these apneic events an analysis of respiratory signals recorded for an entire night's sleep is necessary. However, identifying and classifying apneic events is a complex task, given the error associated with the process for digitising signals, variability in expert criteria and the complexity of the signals themselves. This article describes a fuzzy-logic-based automated system for detecting apneic events and classifying them as apneas or hypopneas. The aim is to equip this system with mechanisms for dealing with imprecision and reasoning affected by uncertainty. The ultimate goal was to assist the physician in diagnosing the sleep apnea/hypopnea syndrome. Results in terms of locating events in the polysomnogram showed sensitivity and specificity of 0.87 and 0.89, respectively. A receiver operating curve index of 0.88 was obtained for the classification of events as apneas or hypopneas.

Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome

Objective Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Approach Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Main results Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. Significance To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

http://iopscience.iop.org/article/10.1088/1361-6579/aaafb8/pdf

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

The sleep apnea syndrome (SAS) is a respiratory disorder, which is characterised by the occurrence of five or more apneic events (apnea or hypopnea) per hour of sleep. Diagnosis of the SAS is a process that is markedly heuristic by nature, in that doctors handle information that is both numerical and symbolic, and employ qualitative descriptive terminology. An expert draws up a contextualised clinical interpretation that relates a patient's sleep process and respiratory physiology, involving a detailed analysis of the polysomnograph corresponding to a night's sleep. This task, implying a great deal of work on the part of clinical staff and a high economic cost, can in fact be partially automated. Our paper describes a modular system based on artificial intelligence techniques that provides an individual SAS diagnosis on the basis of a patient's polysomnograph. The main tasks of our system are the identification and classification of respiratory events, the construction of the patient's hypnogram and the correlation of all the information obtained so as to arrive at a final diagnosis with respect to the existence of the syndrome. Finally our article presents and discusses the results obtained following a preliminary validation of the developed system.

M Castro-Pereiro

Papers

An intelligent system for the detection and interpretation of sleep apneas