Bassel Tawfik

A Fuzzy Logic Model for Medical Equipment Risk Classification

In developing countries, hospitals often suffer from insufficient funds and the lack of qualified technical personnel. This leads to a number of problems, among which is the improper and irregular maintenance of medical equipment. This situation calls for an effective approach to prioritize maintenance jobs based on certain importance criteria to make the best use of the available budget. In this article, we adopt the notion that medical equipment maintenance should be prioritized based on their risk level. Existing risk classification models express risk in terms of equipment function, maintenance requirements, and physical risk. They overlook other important factors such as the operational conditions of equipment. Other models have failed to classify critical devices as high risk because they did not incorporate the criticality of equipment as part of the global mission of the hospital. We argue that because hospitals in developing countries rarely implement coherent management standards, same level-of-care hospitals are not technologically equal. This research proposes a new risk assessment model based on fuzzy logic. Because fuzzy logic is closer to the human way of thinking, it is expected to improve the way devices are prioritized. The proposed model was tested on 136 different medical devices in 4 hospitals. Results show that, in certain cases, the same equipment type may have different risk scores depending on the operating conditions within the hospital. Meanwhile, some mission-critical equipment such as steam sterilizers, electrosurgical units, and hematology analyzers attain higher risk levels than obtained by existing models. We attribute this improvement to the fact that risk scores are now dynamic rather than static.

Symmetry and linearity of trunk function in subjects with non-specific low back pain

Objective. To investigate the effect of a standard rehabilitation program on the symmetry of trunk kinematics in subjects with non-specific low back pain.Design. Assessing lumbar spine kinematics in the cardinal planes using dynamometry.Background. Previous evaluations of trunk symmetry focussed more on anatomical rather than functional symmetry. Correlation of functional symmetry with low back pain was occasionally performed, but only for base line evaluations. To the best of the authors knowledge, there is no study examining the effect of exercise on the functional symmetry of the trunk, especially in non-specific low back pain subjects.Methods. Two groups of young male subjects whose working conditions incurred long daily hours of sitting and standing participated in the study. Muscles of the lumbar spine were initially evaluated in the cardinal planes using dynamometry. The same parameters (maximum isometric torque, dynamic torque, angular velocity and range of motion) were then repeatedly measured throughout a standardized strength protocol lasting for 12 sessions over a four-week period.Results. As pain gradually disappeared over a one-month period of rehabilitation, certain factors of the trunk kinematics exhibited convergence towards perfect symmetry while others showed oscillations. Moreover, global right-left data for certain trunk kinematics exhibited near-perfect linear relationship.Conclusion. Functional symmetry of the trunk in the coronal and transverse planes can be used to assess the progress of rehabilitation programs of non-specific low back pain subjects. RelevanceRehabilitation programs focus on the progress of directly measured trunk kinematics, which do not always exhibit monotonic behavior. This paper alludes to the importance of tracking symmetry of trunk kinematics as it may help clinicians modify the strengthening protocols in order to achieve more rapid relief from back pain.

Benchmarking Clinical Engineering Services in Sudanese Hospitals: Survey and Assessment

Medical equipment is one of the major infrastructure components of hospitals. Their peak performance is critical to the quality of healthcare services. Clinical engineering (CE) services in Sudanese hospitals have been recently existed therefore this paper aims to evaluate the clinical engineering (CE) departments in Sudanese hospitals. Firstly a robust assessment system has been prepared under the name of clinical engineering services assessment system (CESAS). It contains a questionnaire, documentation audit, site assessment and interviews. Then a sample of CE departments is chosen with confidence of 75% and has been appreciated using CESAS. The weaknesses of CE performance, that form the 78% gap with CESAS target, have been reported. This gap tells a serious problem. Investigating and uncovering of its root causes is recommended.

Robust estimation of planar rigid body motion in magnetic resonance imaging

In magnetic resonance imaging, some schemes require slow data acquisition in order to achieve acceptable spatial resolution and signal-to-noise ratio. However, long scanning periods allow patient motion to distort the reconstructed images. In cases of planar rigid body motion, rotational and translational transformations can be assumed to model the patient motion. In this study, we develop a novel technique for estimating the parameters of these transformations. It is based on fixing two markers to the patient and using a post-processing algorithm to estimate the location of these two markers during the scan time. The key factor in this algorithm is to incorporate a priori information about the geometric shape of the markers to achieve accurate estimation. Computer simulation results confirmed the ability of the algorithm to estimate the unknown parameters.

Parameter estimation by reduced-order linear associative memory (ROLAM)

In a series of papers the authors have shown that nonlinear parameter estimation by linear association provides accurate estimates of the parameters in complex systems described by nonlinear differential equations even in the presence of additive white noise of considerable power. The technique is based on linearly associating the systems output with a set of parameter values spanning the region of interest. When an actual output is measured, the systems unknown parameters could be estimated by a matrix inversion. The size of the inverted matrix, being equal to the length of the output vector, poses a limiting factor upon the generalization of the technique. Here, the authors propose a modification which requires the inversion of a matrix whose dimension equals the number of model parameters. The modified version is called reduced-order associative memory (ROLAM). The technique is applied to two complex lumped-parameter nonlinear models: the Van der Pol relaxation oscillator and the passive neuron model of the granule cells. Results validate ROLAM as a parameter-estimation tool which is especially suited in cases where the number of parameters is large, the number of samples in the observation signal is high, or when on-line parameter estimation is required. It is also shown that ROLAM provides an optimal parameter estimate in the special case of single-parameter nonlinear models.

Optimal Design of Emergency Department in Mass Disasters

A disaster situation for a healthcare facility occurs when the need for medical treatment overwhelms the actual treatment capacity. Emergency department (ED) overcrowding occurs because of the sudden patient influx in a disaster situation. Consequences of overcrowding range from death and permanent disability to lengthening of treatment duration. Reduction of these adverse events through better ED design is the main motivation behind this study. There are 2 possible approaches to analyze this problem (1) by measuring patient waiting times in order to determine the bottlenecks in terms of duration and frequency or (2) by improving the physical design of the ED. In this study, we adopt the second approach for both newly designed and existing EDs. We construct new ED design using the facility layout planning algorithms. As for existing designs, we use simulation software in order to analyze the impact of new ideas, rules, and strategies without causing disruptions in ED service and before implementing any changes. The result shows a new construction layout design for ED using facility layout planning algorithm, in a manner conducive to overcoming the overcrowding consequences as much as possible. Also, the patient’s waiting time to receive treatment is decreased by 71% after improving the design for ED in Malta, which suffers from overcrowding by using simulation software. Finally, we do not claim that this is the best design for ED, but we can consider it as a step on the way toward a better ED design.

Reduced Search Space based Association Rule Mining Algorithm

Many algorithms have been proposed to solve the problem of mining frequent itemset. However, the large size of the itemsets search space is still a challenging problem that eliminates the performance of any association rules mining algorithm. The size of search space is exponential to the number of items in the database. In this paper, an effective mechanism is proposed to reduce the search space. Moreover, an efficient algorithm is also devised for mining association rules based on this reduced search space. An accumulative support distribution of 2-itemsets is created over different levels with only one database scan. This distribution provides estimation for the support values of all itemsets in search space. The estimated supports are used in generating the candidate itemsets in all levels without extra database scan. The proposed algorithm reduces the execution time by reducing both CPU and I/O times. CPU time is saved by reducing candidate sets size, whereas the I/O time, is reduced by reducing the required database scans. The experimental and analytical results show a significant improvement of performance up to several orders of magnitude compared to Apriori algorithm. In addition, the number of generated candidate itemsets in proposed algorithm is less than the ones generated by Apriori algorithm in most cases.

Adaptive denoising technique for robust analysis of functional magnetic resonance imaging data

A new adaptive signal-preserving technique for noise suppression in functional magnetic resonance imaging (fMRI) data is proposed based on spectrum subtraction. The proposed technique estimates a model for the power spectrum of random noise from the acquired data. This model is used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data for event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components. Moreover, further analysis using principal component analysis (PCA) and independent component analysis (ICA) shows a significant improvement in both convergence and clarity of results when the new technique is used. This suggests the value of the new technique as a useful preprocessing step for this type of signal.

Strategic directions for university hospitals in Saudi Arabia.

Abstract The kingdom of Saudi Arabia is currently witnessing a massive support for its healthcare services. This year’s fiscal budget for health care is by far the largest in the history of the kingdom. While pursuing the conventional goals of healthcare delivery systems, such as equity, affordability and availability of services, the ministry of education (MOE), formerly called ministry of higher education (MOHE), is also seeking to achieve certain excellence standards, which are expected to set the bar for other countries in the region to follow suit. Almost all, under construction, university hospitals were adopting a paradigm shift in the standard of health care and training provided. This report summarizes these standards.

RFID-based indoors localization of tag-less objects

Object localization has become a necessary module in many radiofrequency identification (RFID) systems that require tracking features besides the conventional identification feature. A number of techniques exists in literature that uses the RFID signal information to locate the tagged objects, i.e. objects wearing RFID tags. Nevertheless, in many applications, it is required to track objects that do not carry a tag (whether intentionally or unintentionally). In this work, we propose a technique for tag-less object localization. The technique is based on reconstructing the attenuation map of the environment using back projection of RFID signals emitted from reference tags fixed on the walls of the monitored space. Numerical simulation as well as real-world data show the feasibility of the proposed method.