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New developments of scientific basic research may be of theoretical as well as of practical significance for forensic medicine. This will be demonstrated for two examples: fuzzy logic and fractal geometry.
The prescription of antithrombotic agents in the elderly depends, to certain degree, on the identity of the unique individual elderly patient. This dependence cannot be captured by viewing the patient as belonging to a group, but rather by viewing age in the context of unique individual biology. This context is historical, physiological, psychological, and time- and location-dependent. The group-based approach to patient therapy is found in evidence-based medicine, typified by the large double-blind randomised clinical trial from which clinical recommendations are defined. An alternative approach to capturing the unique context of each patient is based using fuzzy logic and mathematics. In particular, the fuzzy subsethood theorem of Kosko has direct application here. A new measure of clinical efficiency, K, derived from the fuzzy subsethood theorem has redefined the clinical significance of age. In particular, the causal role of age in any one patient's response to therapy is to unique degree for that patient. This is because the causal measure K accounts for the role of known and unknown contextual factors of the patient, most of which are unknown, in defining clinical effect in any specific patient. Thus, we have shown mathematically why 'age', or being 'elderly', is only one factor to be taken into consideration when therapeutic decisions regarding antithrombotic therapy are made in any given patient. This is contrary to the group-based approach of evidence-based medicine, where age has the same therapeutic significance for all patients. This is because any hypothesis of evidence-based medicine is group-based and cannot be extrapolated to the individual patient. Such extrapolation has to be personalized through the expertise of the physician. The physician takes into account all the factors not considered in any therapeutic group-based trial which apply to his specific patient. These considerations take into account past experience with that patient. An example of the effect of age on the patient and his/her therapeutic context is provided which shows how age affects different patients to different degree using the fuzzy causal measure K. The purpose of this exercise is to show that there is mathematical support for the argument that individualization of patient therapy by expert decision has measurable clinical significance. What is different here is that measure stick is not probabilistic but fuzzy-mathematic based.
The aim of this study was to employ neuro-fuzzy logic and regression calculations to determine the accuracy of prediction of the power output ( P) of the maximal lactate steady-state (MLSS) on a cycle ergometer calculated from the results of incremental tests. A group of 17 male and 17 female sports students underwent two incremental tests (a 1 min test T(1): initial exercise intensity 0.2 W x kg(-1) increasing 0.2 W x kg(-1) every minute; a 3 min test T(3): initial exercise intensity 0.6 W x kg(-1) increasing 0.6 W x kg(-1) every 3 min) and at least four constant-intensity tests of 30 min duration. Two models for MLSS calculation were developed using the data from T(1) and T(3), a forward stepwise linear regression model (REG) and a neuro-fuzzy model (FUZ). A group of 26 randomly selected subjects (model group, MG) were used to generate the REG and the FUZ models. The data from the remaining 8 subjects (4 men and 4 women; verifying group, VG) were used to verify the REG and FUZ models. The precision of the MLSS calculation in MG produced a better correlation when using data from T(1) (REG r=0.95, FUZ r=0.99) than data from T(3) (REG r=0.88, FUZ r=0.98). Our calculation models were confirmed using data from VG for T(1) (REG r=0.97, FUZ r=0.98) as well as for T(3) (REG r=0.97, FUZ r=0.97). Based on our subject population of young, healthy sport students, our results suggest that a single incremental test may be used for prediction of P at the MLSS using a cycle ergometer. Furthermore, the results from T(1) yielded higher correlations compared to T(3). Calculations from REG were similar to FUZ but the precision of REG and FUZ was better compared to calculations derived using data from a single threshold.
We have assessed the practicality and performance of the Vital Signs Paragraph neuromuscular blockade monitor as part of a 'self-learning' fuzzy logic control feedback system used to administer atracurium to a required depth of neuromuscular blockade. Fifteen patients undergoing surgery expected to last longer than 90 min entered the study. A Vital Signs Paragraph was used to measure the degree of neuromuscular blockade and control it such that the first twitch of the train-of-four was kept at 10% of its baseline value. The controller instructed a Graseby Medical 3400 infusion pump to administer an atracurium infusion to maintain this level of blockade. Five patients (33%) were withdrawn from the study due to inadequate piezo-electric sensor function. In the remaining 10 patients, the system achieved stable control of neuromuscular blockade with a mean (range) error for the first twitch of the train-of-four of -0.45 (-1.06 to 0.13)%. The mean atracurium infusion rate ranged from 0.13 to 0.67 mg.kg-1.h-1. These results compare reasonably well with previous results using the Datex Relaxograph, whilst the system itself was portable and easy to use. However, the reliability of the system was limited due to variability in the sensitivity of piezoelectric sensors.
We propose a maximum a posteriori image restoration approach to 3D confocal microscopy. The image field is suitably modeled as a Markov random field, resulting in a Gibbs distributed image. A fuzzy-logic-based potential is employed in the Gibbs prior. Unlike other potentials, the fuzzy potential distinguishes intensity variation due to genuine edges and noise. The proposed approach has generated artifact-free restored confocal microscopy images.
A patient supervision system in progress for intensive and coronary care units, focused on patients with acute myocardial infarct is briefly described particularly regarding the role that fuzzy logic is playing in its design, and why this is so.
During the last few years intelligent machines appeared in nearly all technical areas, such as consumer electronics, robotics, and industrial control systems. There are for example washing machines that work very effectively, need comparably less power than in the past, and have short execution times because they adjust their washing cycles to each set of clothes and change their washing strategies as the clothes become clean. These intelligent systems are based on fuzzy control strategies, i.e., common sense rules are used to describe a system's behavior instead of complex mathematical models. We have applied this new technology to control problems as well as to reasoning problems in biomedical engineering where appropriate mathematical models could not be built due to the complexity of the problem. After a short introduction to the concepts of fuzzy logic two approaches in the field are described: a fuzzy control strategy for the pump rate adjustment of a novel total artificial heart and an intelligent alarm system based on fuzzy inference which supports the anesthetist in monitoring and evaluating the hemodynamic state of a patient undergoing cardiac surgery. These examples indicate the inherent reliability and stability of this technique in the field of complex dynamic systems. Such properties are highly significant especially in medical applications.
Recently, the combination of sliding mode and fuzzy logic techniques has emerged as a promising methodology for dealing with nonlinear, uncertain, dynamical systems. In this paper, a sliding mode control algorithm combined with a fuzzy control scheme is developed for the trajectory control of a command guidance system. The acceleration command input is mathematically derived. The proposed controller is used to compensate for the influence of unmodeled dynamics and to alleviate chattering. Simulation results show that the proposed controller gives good system performance in the face of system parameters variation and external disturbances. In addition, they show the effectiveness of the proposed missile guidance law against different engagement scenarios where the results demonstrate better performance over the conventional sliding mode control.
Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2(4) full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl2 flow rate. The other called multipole ICP was characterized by performing a 3(3) full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density. electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.
The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes the segmentation as a preliminary stage for the visualization or the quantification of such data. In this paper, we present a fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The presented method consists of two main stages. The histogram analysis based on the S-function membership and Shannon's entropy function is the first step. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. After the segmentation is complete, attributes of different tissue classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. The implemented system provides many advanced 3D imaging tools, which enable visual exploration of segmented anatomical structures.
One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.
An algorithm has been developed that can be used to divide triangulated molecular surfaces into distinct domains on the basis of physical and topographical molecular properties. Domains are defined by a certain degree of homogeneity concerning one of these properties. The method is based on fuzzy logic strategies, thus taking into consideration the smooth changes of the properties considered along complex macromolecular surfaces. Scalar qualities assigned to every node point on a triangulated surface are translated into linguistic variables, which can then be processed using a special fuzzy dissimilarity operator. Possible applications are demonstrated using surface segmentation for properties like electrostatic potential, lipophilicity and shape for the analysis of serine proteinase substrate/inhibitor specificity.
Achieving good performance in air supply control is an important goal in the management of wastewater treatment plants, whose highly nonlinear behaviour makes the application of conventional control techniques problematic. This paper presents the development and experimentation of a fuzzy logic system for air supply regulation in a full scale municipal wastewater treatment plant. The system is composed of two main modules, one devoted to continuously adjusting the DO set point on the basis of the current effluent NH4(+)-N concentration (on-line measurement), and the other devoted to achieve the DO set point by controlling air supply devices. The experiment was carried out on the plant for about one year, leading to significant advantages in terms of both process stability and energy saving.
Computerized clinical guidelines can provide significant benefits in terms of health outcomes and costs, however, their effective computer implementation presents significant problems. Vagueness and ambiguity inherent in natural language (textual) clinical guidelines makes them problematic for formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. In care plan on-line (CPOL), an intranet-based chronic disease care planning system for general practitioners (GPs) in use in South Australia, we formally treat fuzziness in interpretation of quantitative data, formulation of recommendations and unequal importance of clinical indicators. We use expert judgment on cases, as well as direct estimates by experts, to optimize aggregation operators and treat heterogeneous combinations of conjunction and disjunction that are present in the natural language decision rules formulated by specialist teams.
The nursing diagnoses associated with alterations in urinary elimination require different interventions, and nurses who are not specialists need support to diagnose and to manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for making differential diagnosis of alterations in urinary elimination, considering the nursing diagnosis approved by the North American Nursing Diagnosis Association (NANDA), 2001-2002. The fuzzy maximum-minimum composition was used to develop this model. It was tested with 195 cases from a database of a previous study. The model was able to determine the diagnosis in total accordance with a panel of three experts for 79.5% of the cases. The model diagnosed 19% of the cases with partial concordance with the panel of experts. Only for 3 cases (1.5%) the model showed a different diagnosis. It is concluded that the model proposed here, despite of its simplicity, presents good performance. However, it is recommended more tests before widely used as support for clinical decision.
BACKGROUND: Preoperative evaluation of patients with potentially resectable non-small cell lung cancer aims to estimate the risk of planned surgery. Evidence of several factors that identify patients at risk for complications from thoracotomy is controversial. The aim of this study was to introduce and implement in medical practice a fuzzy system used in risk assessment of pulmonary resection for lung cancer. METHODS: Ninety-one consecutive patients who underwent pulmonary resection for lung cancer were investigated. The overall complication rate was 39.6 % (a total of 63 complications were seen in 36 patients). A fuzzy logic model was created with 9 input (presence of chest pain, weight loss, clinical T stage of the tumor, FEV 1, serum protein, preoperative arterial partial oxygen pressure and cigarette smoking, erythrocyte sedimentation rate and peripheral blood leukocyte count) and two output classes (high-risk and low-risk groups). The fuzzy classifier's performance was tested. RESULTS: The model was able to predict correctly the occurrence of complications in 22 out of 29 patients in the high-risk group with a sensitivity of 76 %, while 9 out of the 52 patients from the low-risk group developed complications (17 %). CONCLUSION: The fuzzy classification system provides an accurate tool to predict complications of resections in patients with non-small cell lung cancer.
Breast cancer is still a serious disease in the world. Early detection is very essential for breast cancer prevention and diagnosis. Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions. Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis. This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement. We normalize the images and then fuzzify the normalized images based on the maximum entropy principle. Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified. The defuzzification process is used to obtain the enhanced US images. To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images. Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement.
BACKGROUND: The State of São Paulo, the most populous in Brazil, was virtually free of measles from 1987 until the end of 1996 when the number of cases started to rise. It reached alarming numbers in the middle of 1997 and local health authorities decided to implement a mass vaccination campaign. METHODS: Fuzzy Decision Making techniques are applied to the design of the vaccination campaign. RESULTS: The mass vaccination strategy chosen changed the natural course of the epidemic. It had a significant impact on the epidemic in the metropolitan area of São Paulo city, but a second epidemic in the State's interior forced the public health authorities to implement a second mass vaccination campaign 2 months after the first. CONCLUSIONS: Fuzzy Logic techniques are a powerful tool for the design of control strategies against epidemics of infectious diseases.