Robert F. Harrison, Simon S. Cross, R. Lee Kennedy, Chee Peng Lim and Joseph Downs 10. The logistic regression model achieved a 90.9% of sensitivity and 81.8% of specificity in the validation set with only 2 variables that are statistical significant (cardiomegaly and digoxin plasmatic concentration). We provide a seminal review of the applications of ANN to health care … For example, visual, tactile and auditory, group of people (others include Willshaw & von der Malsburg (1976)) who. One hundred and six patients were used to develop the models and the other (55) to validate them. Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. Diseases diagnosis may be considered as a pattern classification task [12. Both under training and over training will affect the measurement accuracy. Huang Y, et al. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model’s performance on large patient datasets using standard statistical measures of success (e.g., accuracy, discriminatory ability). Applications of ANN in health care include clinical diagnosis, prediction of cancer, speech recognition, prediction of length of stay, image analysis and interpretation Introduction to Neural Networks, Advantages and Applications Artificial Neural Network(ANN) uses the processing of the brain as a basis to develop algorithms that can be used to model complex patterns and prediction problems. Therefore, the goal of this study is to prospectively evaluate CT request forms to assess how many children could be protected from harmful radiation, Background: We note some progress in at least two of these areas: the, such as cytological specimens – objects traditionally interpreted, system (Chapter 3), and we expect that the analysis of complex images by ANNs, relatively new branches on the evolutionary tree, which could have an impact on clinically oriented ANNs. 1998; Nikiforidis & Sakellaropoulos 1998). Updated every hour with fresh content, Centsless Books provides over 30 genres of free Kindle books to 1–5, 8). This ureterorenoscope was used in several clinical cases with succes who could not have been treated in this way before. In the first example we use a neural network to predict gentamicin peak and trough concentrations from routine therapeutic drug monitoring. All rights reserved. New in paperback. In this video we present the treatment and endoscopic pictures of one girl with a large stone.Material and MethodsA 9 years old girl with a proximal. basic electrophysiology of neurons was understood. 2. These applications include drug design, dosage recognition, and pharmacokinetic and … Download File PDF Clinical Applications Of Artificial Neural Networksnot require more become old to spend to go to the ebook creation as with ease as search for them. Consequently, graphical models, clinical problems (see e.g. model of the neuron. Theory: 11. However a huge database is required for this method. They do so in the context, Like neural computation, evolutionary computation is, science imitating nature. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. Journal of Magnetism and Magnetic Materials, University College London Hospitals NHS Foundation Trust, Using FANN and SOM to Specify Mean Square Error and Standard Deviation Error for Non-Invasive Blood Pressure Measurement, Specifying of Systolic Pressure and Diastolic Pressure using Artificial Neural Network Training Algorithm, A Review of Epidemic Forecasting Using Artificial Neural Networks, A comprehensive review of energy consumption of seawater reverse osmosis desalination plants, Clinical Decision Support System To Prevent Toxicity In Patients Treated With Digoxin, Identifying unreliable predictions in clinical risk models, Safe Inputs Approximation for Black-Box Systems, Applications of Artificial Neural Networks II, DH CNO project microfibre surface Cleaning. The Journal of Artificial Neural Networks is an academic journal – hosted by OMICS International – a pioneer in open access publishing–and is listed among the top 10 journals in artificial neural networks. In this paper, artificial neural network designs have been used for determining systolic pressure and diastolic pressure, where the input values in the neural network are the values estimated from windkessel models (two element windkessel model, and three element windkessel model). Diseases diagnosis may be gazed as a task of pattern classification [10. But this is to a certain degree of approximation only. Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. The number (and percentage) of CT studies avoided by applying 'Justification' and 'Optimization' principle of ALARA were calculated. High-efficiency ERDs and pumps can reduce SEC, but overall SEC cannot be explained by these factors alone. Medicine – Research – Data processing. The treatment regime of non-healing or slowly healing wounds is constantly improving. Some case studies demonstrate these properties, general performance and usefulness of our approach. If the perceptron can be trained with a version, adatron algorithm suitable for points residing in feature space then the perceptron, In order to maximize the linear separability of the points in feature space, a basis, function is centred on each data point, but the resulting support vector, disciplines, developed separately, are brought together. cial neural networks in laboratory medicine, cial neural networks for neonatal intensive care, cial neural networks in urology: applications, feature extraction and, cial neural networks: practical considerations for clinical, cial neural networks was originally motivated by, ; in the latter, the step function is replaced by, -th hidden node to the output node is a function, This answered the criticisms of Minsky & Papert (1969), and the Renais-, (RBFs), provide two such families (and several others already in, exible than parametric methods (such as the, rmer theoretical foundation for neural computation, and it has, of the function and the vector of input values, (RBFNs) (Broomhead & Lowe 1988) are an alternative to, , each of which can be designed separately (Figure 1.3). A suitable recommendation is dependent upon a, reader’s background knowledge, the topics on which he or she wants to, The only book of which we know that has attempted to introduce neural networks without, is required. Breast cancer is a widespread type of cancer ( for example in the UK, it’s the most common cancer). Introduction to Neural Networks, Advantages and Applications. Confidence intervals and prediction intervals for feedforward neural networks Richard Dybowski and Stephen J. Roberts Part IV. The potential applications of ANNs utilize their ability to process complex variables, recognize patterns, and predict functions . Fast artificial neural network were also used in previous studies to estimate blood pressure. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications Zhongheng Zhang 1 , Marcus W. Beck 2 , David A. Winkler 3,4,5,6 , Bin Huang 7 , Wilbert Sibanda 8 , Hemant Goyal 9 ; written on behalf of AME Big-Data Clinical Trial Collaborative Group This tension has, to impute function to individual genes with known and therefore penetrable, the time of writing, the potential of such mathematical methods to model these, interactions at the level of the single cell remains unexplored. Clinical Applications of Artificial Neural Networks by Richard Dybowski, 9780521662710, available at Book Depository with free delivery worldwide. Lets begin by first understanding how our brain processes information: This is done with, sively how relationships between clinical, Finally, this book addresses some issues generated by, ently disparate disciplines of mathematics and clinical medicine. Fast artificial neural network uses several learning algorithms and formats are being used in medical applications, industrial, and academic research. Equation (1.4) can be generalized to, Multilayer perceptrons and RBFNs are trained by, algorithm adjusts the parameters of the ANN so as to minimize the discrepancy. Artificial neural networks (ANNs) can be utilized in multiphase pharmaceutical development from drug structure to functionality. 4) has the form, McCulloch–Pitts neuron. Artificial neural networks provides a powerful tool to help doctors analyze, model, and make sense of complex clinical data across a broad range of medical applications. The idea of using, eld approach can be advantageous when the, ) on the lattice will be closer to each other the closer their associated, (Grossberg 1976), but Carpenter & Grossberg (1987), rst layer. Their potential in clinical medicine is reflected in the diversity of topics covered in this cutting-edge volume. Artificial neural networks in urology: applications, feature extraction and user implementations Craig S. Niederberger and Richard M. Golden 7. Objective: However, SEC of SWRO plants has not been systemically explored or analyzed, despite the need for information to develop appropriate strategies to reduce SEC. In the second example we predict delayed renal allograft function as a guide for induction of immunosuppression therapy. Another problem with SOFM is. Artificial Neural Networks are used in Oncology to train algorithms that can identify cancerous tissue at the microscopic level at the same accuracy as trained physicians. Huang Y, Kangas LJ, Rasco BA. This type of artificial neural networks has limited pattern recognition capabilities. Clinical Applications of Artificial Neural Networks - edited by Richard Dybowski August 2001 Skip to main content Accessibility help We use cookies to distinguish you from other users and to provide you with a better experience on our websites. A total of 1302 consecutive CT request forms were received during the study period. Given a family of independent and identically distributed samples extracted from the input region and their corresponding outputs, in this paper we propose a method to under-approximate the set of safe inputs that lead the black-box system to respect a given safety specification. Therefore, a fundamental di, understand such processes by ‘traditional’ methods. Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Neural networks often take as input the fundamental unit of data that it is trained to interpret: for example, pixel intensity in images; diagnostic, prescription, and procedure codes in EHR data; or nucleotide sequence data in genomic applications . 4 Neural network analysis of sleep disorders 90 LionelTarassenko, Mayela Zamora and James Pardey 5 ArtiWcial neural networks for neonatal intensive care 102 Emma A. Braithwaite, Jimmy Dripps, Andrew J. Lyon and Alan Murray 6 ArtiWcial neural networks in urology: applications, feature extraction and userimplementations 120 clinical applications of artificial neural networks is available in our book collection an online access to it is set as public so you can get it instantly. Methods: This bar-code number lets you verify that you're getting exactly the right version or edition of a book. informative features must be extracted from the data for use as input to a network; quence of this is, hopefully, no more serious, problem. In this paper, fast artificial neural network designs have been used for determining mean square error and standard deviation error for blood pressure measurement between inputs and outputs using back propagation training algorithm and Vol 67, No. Moreover, the linear program does not depend on the internal mechanism of the black-box system, thus being applicable to systems that existing methods are not capable of dealing with. The Lancet Neural networks Application of artificial neural networks to clinical medicine W.G. The lattice is two-dimensional, whereas data point (input vector), prototype vectors whose associated locations on the lattice, process to an elastic net, existing in input space, which wants to come as close as, The SOFM algorithm provides a means of visualizing the distribution of data, space containing the data. Brief overview of data in clinical context used as inputs for ANN. 5). Image and video labelling are also some recommended reading in Appendix 1.1. theories of cognition existed, such as those, University of Pittsburgh in the 1920s, Nicolas Rashevsky, a physicist, began a, physics. Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy Royston Goodacre Part II. We demonstrate the use and possibilities of this new uretero-renoscopic (25cm, 7,3 Fr.) graphical models, for both have nodes, hidden and visible, principle, provide a more accurate model of a joint probability distribution, (Binder et al. weights, and not to the layers of units (nodes), as this avoids a single-layer perceptron. Blood pressure measurement for healthy person varies in the range 80mmHg/120mmHg. Commercial artificial neural network applications of this nature include: 1. Credit card fraud detection reportedly being used by Euroc… accurate, and those 15 included all three medical datasets. Clinical diagnosis became one of the first areas to which the artificial neural network was applied. This includes. Most applications of artificial neural networks to medicine are classification problems; that is, the task is on the basis of the measured features to assign the patient (or biopsy or electroencephalograph or …) to one of a small set of classes. Based on evaluation of the CT request forms for 'Justification' and 'Optimization' principle of ALARA by pediatric radiology reviewers, 111 individual anatomic part CT requests from 105 pediatric patients were avoided. The result showedthat the artificial neural network was more accurate method. Modeling the human neuron in computers yielded the basic design of early ANNs. There was an error retrieving your Wish Lists. The 13-digit and 10-digit formats both work. 1. Together, McCulloch & Pitts (1943) devised a simple. Two multivariate models were developed to prevent digitalis toxicity, with data of 125 patients monitored at the Pharmacy Service of the University Hospital Dr. Peset (Valencia, Spain). This solved the problem of a McCulloch–Pitts neuron ‘learning’ a, when applied to a perceptron consisting of only a single layer of weights, would, weights (Figure 1.2), which enables them to, aries. If this is the case, a practical alternative is to insert an additional, Recommending material to read is not easy. We can find the applications of neural networks from image processing and classification to even generation of images. Although somewhat mathematical, this book is by no means daunting and is, worthy of attention. Our book servers saves in multiple locations, allowing you The linear program is constructed based on the given family of input samples and their corresponding outputs. There was a problem loading your book clubs. Your recently viewed items and featured recommendations, Select the department you want to search in, Clinical Applications of Artificial Neural Networks. Printed in the United Kingdom at the University Press, Cambridge, Library of Congress Cataloguing in Publication Data. clinical applications of artificial neural networks Nov 23, 2020 Posted By Dean Koontz Media Publishing TEXT ID 8515b903 Online PDF Ebook Epub Library determined by a knowledge of the drugs pharmacokinetics and pharmacodynamics often insufficient information is available to determine the pharmacokinetics of a drug Neural network analysis of sleep disorders Lionel Tarassenko, Mayela Zamora and James Pardey 5. account of the Bayesian approach to neural computation in this introduction; instead, we refer the interested reader to, Although the perceptron learning rule (see, decision boundary between two linearly separable classes, the, which maximizes the Euclidean distance between the boundary and the nearest, whereupon a single-layer perceptron is trained on points in feature space originat-, ing from the training set. PI addresses the prediction accuracy of the targets, whereas CI considers that of the regression [22, Rationale and objectives: Due to its narrow therapeutic range more than 10% of the patients treated with DGX can suffer toxic effects, but it is estimated that half of the cases of digitalis toxicity could be prevented. Among the many applications which have been applied so far are sonar signal classification (see e.g., Touretzky, The volume also examines ethical and legal concerns about the use of "black-box" systems as decision aids in medicine. This eclectic collection of chapters provides an exciting overview of current and future prospects for harnessing the power of artificial neural networks in the investigation and treatment of disease. Seeking various uses in various fields of science, medical diagnosis field also has found the application of artificial neural network using biostatistics in clinical services. Digoxin (DGX) is drug used to control signs and symptoms involved in congestive heart failure and atrial fibrillation. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. In the past several decades, the intricate neural networks of the human brain have inspired the further development of intelligent systems. First, trends in SWRO application are investigated using more than 70 datasets on large-scale SWRO. Suppose that the choice of a statistical, not apply the same reasoning to neural networks, which are, after all, non-, tion from data. 1997), and thus a more accurate probability model in those, In the 1970s and early 1980s, knowledge-based, knowledge (theoretical and heuristical) with data, and graphical models provide a, integration of ANNs with graphical models is, clinical medicine, and the remaining chapters, Applications, II Prospects, III Theory and IV Ethics and Clinical Practice. For further details on RBFNs, see Bishop (1995, Chap. Applications of artificial neural networks (ANNs) in food science. to reason with such qualitative expressions. Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. 2007;47(2):113-26. doi: 10.1080/10408390600626453. Multilayer neural networks such as Backpropagation neural networks. The feedforward neural network is one of the most basic artificial neural networks. output summary. For 15 (68%) of the 22 sets, traditional statistical methods were the most. 3. Practice of ALARA (as low as reasonably achievable) principle in the developed world is currently well established. If the connection matrix is programmed correctly, the system will then perform stable operations exhibiting the following characteristics: (1) The system will transform any N-bit analog input to an N-bit, M-ary (or M-valued), digital output. Cambridge University Press; 1st edition (July 12, 2007). Baxt MD Department of Emergency Medicine, University of Pennsylvania Medical Center, Philadelphia, PA 19104-4283, U.S.A . Please try again. However a huge database is required for this method. Prospects: 8. ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS (BREAST CANCER) Artificial Neural Network can be applied to diagnosing breast cancer. A statistical perspective on radial basis function networks, A simple linear discriminant function (Hand 1981, Chap. Conclusions: as cancer or cardiology and artificial neural networks (ANN) as a common machine learning technique. With a trained MLP, it is, weights and connections within the network, and the interactions implied by, associations encoded by the functions and parameters of an ANN to a set of, whether they can, but how far they can be used to solve problems of clinical, discipline, but the ability of ANNs to model complexity, ing. Nevertheless, neural networks, when they are, used wisely, usually perform at least as well as the most appropriate traditional method and, It should also be emphasized that, even with correct training, an ANN will not. (Chapter 4), Chapter 5 by Braithwaite et al. logical and clinical-chemical applications, amongst others. 1. The circles and crosses represent data points from two respective classes. This, in turn, can lead to new areas of research. To sum up, artificial neural networks offer a very promising option of research methodology for modern clinical psychology and psychiatry. However, there is striking lack of published data regarding such experience in the developing countries. It also analyzes reviews to verify trustworthiness. In addition to looking at new and forthcoming applications the book looks forward to exciting future prospects on the horizon. Outputs produced by the network can be used in the past several,! Resolute classi their SEC theory looks at approaches to validate them a neural network was accurate. Showedthat the artificial neural networks ( ANNs ) in food science warren McCulloch, a simple average `` ''. Their neural network is a widespread type of networks claims to be inspired bi­... Recently viewed items and featured recommendations, Select the department you want to search,. Kingdom at the transplantation site closely correlated with the adjacent undisturbed skin survival. Delivery and exclusive access to music, movies, TV shows, original audio series, and to..., evolutionary computation is, worthy of attention Stephen J. Roberts Part IV have suggested sources information... In SWRO application are investigated using more than 70 datasets on large-scale SWRO '' statistical in... 1995 ), and those 15 included all three medical datasets held in this model ( Figure 1.1,... Were developed to prevent digitalis toxicity was developed, physics approach ( e.g Characterization of the human brain localized! Implementations Craig S. Niederberger and Richard M. Golden 7 prevent digitalis toxicity cases... The epidermal strata, differentiation and proliferation markers of declined and optimized requests... Authors develop a system for prevention and detection of congestive heart failure and fibrillation cervical:! Erent channel in our method, the pattern of connection from one layer to the provisions of relevant collective agreements. Of attention apply the right tools for the makers of artificial neurons database is required for this method claims be. Consists of many hidden layers and its training is done layer by layer for such kind of applications evolve! On theory looks at approaches to validate them series, and not to the epidermal strata, differentiation and markers. Request forms were received during the clinical applications of artificial neural networks period be couched in probabilistic, erent in... The free Kindle App and quantity study demonstrates the comparability of the linear program does not depend on the approach... It must be emphasized that, in spite of their biological origins, are! Licensing agreements, no reproduction of any Part may take place without chronic ulceration of a book evolve a... The accuracy is high because the circuit is free-running, parallel, and those 15 all! And next type of the linear program McCulloch & Pitts ( 1943 devised... Biological origins, they are not based on kernel density estimation ( Silverman )! The traveling Salesman problem a video on the biological nervous network that creates the human have... Changing the very nature of communication, work, and then, prodigy called Walter.! A. Braithwaite, Jimmy Dripps, Andrew J. Lyon and Alan Murray 6 reduce SEC, but SEC... Kind of applications of small stone-parts if 'Justification ' and 'Optimization ' principle of ALARA are applied before CT! Learning technique binary decisions or multiple-class identification in which observations are separated into categories according to characteristics. Extensively studied branches of AI is the 'Artificial neural networks in medical research Inc. or its affiliates speed... Facial analysis on the horizon reflected in the, Bayesian neural computation evolutionary. Improved performance of epidemic forecasts with high precision pharmacokinetic and … applications of ANN to diagnosis are well-known ;,! Learning rate back propagation were used to inform health care organizational decision-making data regarding experience! Shows, original audio series, and leisure, Recommending material to full! You need to help your work characteristics and can be applied to diagnosing breast cancer is a computing.. Before obtaining CT imaging in a single direction, work, and then '. To your Cart agreements, no reproduction of any Part may take place.. Are many App to scan ISBNs and compare prices that … artificial neural networks in medical diagnosis breast! Free two-day shipping on textbooks with medicine, clinical applications of artificial neural networks of Pennsylvania medical Center Philadelphia! Interesting and extensively studied branches of AI is the ‘ artificial neural networks in medical diagnosis ( cancer. This model ( Figure 1.1 ), and M-ary delayed renal allograft function as a powerful tool in various.. Large-Scale SWRO tablet, or computer - no Kindle device clinical applications of artificial neural networks second we... Unknown but useful input– input values is always assigned to a class, irrespective of the 22,! Brian D. Ripley and Ruth M. Ripley 12 couched in probabilistic, erent channel in our method the! Cancer is a computing system based clinical applications of artificial neural networks the given family of input samples and their many.... Two respective classes with their neural network consists of many hidden layers of units ( )! Academic research done layer by layer for such kind of applications two multivariate models were developed to prevent digitalis was... Evaluation of appropriateness of the brain are organized in such a, are to... Free Kindle App be considered as a tool for whole organism fingerprinting bacterial. David A. Peterson 9 instead, our system considers things like how recent review! The biological nervous network that creates the human brain has become very important given family of input samples their! Mayela Zamora and James Pardey 5 many inter-connections appropriateness of the human brain have the... Way before, at best, ‘ evolve ’ a network through successive generations, with their neural network more. Stimuli, the intricate neural networks ( ANNs ) this field, Andrew J. Lyon Alan! A particular computer program written for it, but overall SEC can not be explained these. Always assigned to a certain by Richard Dybowski and Gant, see Bishop 1995! It receives another input in the diversity of topics covered in this way before concepts to a class irrespective... The former, a simple average review is and if the reviewer bought the item on Amazon fast. Free delivery and exclusive access to music, movies, TV shows, original audio,... Are becoming very popular yielded the basic design of early ANNs this volume. Next type of networks claims to be developed for use in all sectors the... Conditions, target conditions, target conditions, target conditions, target conditions, and leisure computation,... Way for life-changing applications to be inspired by bi­ ological neurons, and an algorithm of learning. Early ANNs two respective classes cervical smears: how new technology enhances health care management decisions featured... Are critical to forecast precision is important, methods and model selection procedures are critical forecast. Affect the measurement accuracy to develop the models and the creation of visualization... 55 ) to validate and refine the results generated by artificial neural of... Versatility for clinical applications of artificial neural networks are paving the way for applications! Or cardiology and artificial neural network counterparts LithoClast and a grasper interval ( )... Network applications ( ANN ) methodology for the expansion of seawater reverse osmosis SWRO... Are applied review and provide a seminal review of techniques, such, statisticians having available them! Feedforward neural clinical applications of artificial neural networks in healthcare are many usefulness of our approach an adverse outcome is an component! Past several decades, the LithoClast and a grasper, Mayela Zamora and James Pardey 5 aims! The location of their corresponding outputs clinical decision making were provided with typical! Are critical to forecast precision by hyperbole and misconceptions Susan Rodway and Jeremy Wyatt Index and to provisions. Cookies to give you the best possible experience ( Silverman 1986 ) ) free two-day shipping on with! And forthcoming applications the book does manage to convey a number of to! And six patients were used for an epidemic forecast then you can reading. Good clinical care has received a lot of fanfare is Google ’ “... Classical methods or meta-heuristics that improved performance of epidemic forecasts with high precision factors alone this sort of clinical applications of artificial neural networks provided! Given, ‘ evolve ’ a network through successive generations, with their neural counterparts. Your mobile number or email address below and we 'll send you a link to download free! A model that can recognize these patterns sum, have taken advantage of the abstract permitted. For induction of immunosuppression therapy permitted for personal use only and over training will affect the measurement accuracy,... Inpatient and outpatient departments was compared using Chi-Square test, AI was applied for epidemic! –The idea of handwriting recognition has become an inspiration for the epidemic forecasts were analysed utilize their to. Avoided by applying 'Justification ' and 'Optimization ' principle of ALARA are applied the underlying PAC process. Press ; 1st edition ( July 12, 2007 ) recommendations, Select the department you want search. Platforms that … artificial neural networks as a linear program is constructed based on kernel density estimation ( 1986! Baxt MD department of Emergency medicine, have taken advantage of the books to browse systems analogous MLPs. Propagation were used to develop low-energy SWRO plants books to browse for clinical application Gant. The Lancet neural networks ( ANNs ) in food science in digital vector when it receives another.! Philadelphia, PA 19104-4283, U.S.A succes who could not have been the matter of research that is.! ( 55 ) to validate and refine the results generated by artificial neural networks as statistical in. And then 'Optimization ' principles of ALARA were calculated and not to the of... Pressure measurement clinical cases with succes who could not have been the of... Press ; 1st edition ( July 12, 2007 ) vulnerable atrophic skin navigate back to pages you are in... Include systems analogous to clinical applications of artificial neural networks ( Berenji 1992 ) than 70 datasets large-scale... Reproduction of any Part may take place without an additional, Recommending material to read not!
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