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Malegra FXT Plus

By Z. Emet. College of Saint Mary. 2019.

This type of system is also called rules based expert system and it is the most used system for imple- menting medical diagnosis [2] buy malegra fxt plus overnight delivery. It has a graph structure and a chain logical evalua- tion is applied on this structure buy malegra fxt plus visa. Such an expert system could be easy to implement and also very easy to use for a non-engineer because its rules are similarly with the natural medical language malegra fxt plus 160mg without prescription. For hepatitis diagnosis it is necessary to specify which are the factors that define different types of hepatitis. There is a set of markers that have to be analyzed in order to decide what type of hepatitis is present in a patient organism. The logical model consists of the following rules, which are created using the markers that appear in Table I: R1: If M1 and M3 then B R2: If M1 and M4 then B+D R3: If M2 and M5 then C Fig. Fre- quently, it is hard to express the rules for the system and also the translation of implicit knowledge into explicit rules would lead to loss and distortion of infor- mation content [3]. On the other hand, the tree structure of rule-based relation- ships becomes too complex if new levels of knowledge are added. For example, there are many types of hepatitis B and if the system described before has to de- cide between these types, it will be difficult to implement it. A problem that must be taken into consideration is linked to the fact that inferences are done based on the informa- tion contained in a sample, which is only a part of the whole population. The probabil- ity plays an important role, being used to define the quality of an affirmation, to measure the uncertainty or to describe the chance for an event to happen. In this area, the most frequently used method is the Bayes’s theorem, which sets a probabilistic value for each considered output (disease, if the system is applied in medical diagnosis). Bayesian networks have an important area of applicability in the entire field of artificial intelligence, setting a posterior probability when prior probability is known [4]. The analysis starts with the prior probabilities (preceding the experience) for the interesting events. Then it is used a supplementary information from a sample, a test, a report or from other sources, information that affects the prob- ability of the events. The prior probability will be revised using this new informa- tion and the result will be the posterior probability (after the experience and based on the experience). Prior probabil- ity Bayes’s theorem Posterior probability New informa- tion Figure 3 Bayes’s theorem There are three evolutional types of hepatitis B (usual, with relapses and with de- compensations) and six grades of disease (easy, medium, grave, prolonged, cho- lestatic and comatose). It is very useful to have an expert system that can predict, using symptoms and laboratory test results, what type and what form of hepatitis B is present for a new patient. It needs a database with symptoms for a number of patients (Ω - statistical population) that have associated a final diagnosis set. In this application was used a database with over 150 patients with hepatitis B virus infection. Medical Predictions System Bayes’s theorem is a formula with conditioned probabilities. If it is applied in medical diagnosis, its form is: p( S | Dk )⋅ p( Dk ) p( Dk | S ) = (1) p( S ) where Dk is a disease and S a set of symptoms. Using the theorem it can be calcu- lated, for a patient, the probability of appearance for each disease Dk when the set of symptoms S is present. If it is supposed that a patient suffers of only one disease at a moment, then the following formula could be used: m p( S ) = ∑ p( S | D j )⋅ p( D j ) (6) j=1 where j is an index of all investigated diseases δ1, δ2, …, δm. This formula will be applied for each evolutional type and each form of hepatitis B disease, offering for each one a plausibility score. Such an expert system could be successfully used if it is developed for mutual exclusive diseases and independent symptoms. But sometimes these restrictions cannot be accomplished because there are situations when some symptoms have the same cause (being connected) and a patient can suffer of more than one dis- ease. It was also observed that Bayes’s theorem needs an excessive calculation time if statistical population Ω is very large. In order to avoid these problems, two other statistical algorithms were implemented: Aitken’s formula and Logistic model. Aitken’s formula [5] is an alternative for equation (3) (which is the most time con- sumer in Bayes’s theorem). The probability p( S | Dk ) can be quickly found if this formula is used: T 1 n−st st p( S | Dk ) = ∑ λδ ⋅(1 − λδ ) , k = 1,. Medical Predictions System p( E ) p( E ) o( E ) = = (10) p( E ) 1 − p( E ) and conditioned anti-probability: p( E | F ) o( E | F ) = (11) p( E | F ) From (10) and (11), where E and F are two events, can be written equations (12) and (13): o( E ) p( E ) = (12) 1 + o( E ) o( E | F ) p( E | F ) = (13) 1 + o( E | F ) It is easier to calculate o(E|F) than p(E|F). Logistic discrimination will be used in order to find the logarithm of the anti-probability of disease Dk conditioned by the vector S: n lno( Dk | S = s ) = w0k + ∑ wik ⋅ sign( σi ) (14) i=1 where: n – the number of symptoms; m – the number of diseases; k = 1, …, m; wi – are called ‘weights’ and they are calculated with the equations (15) and (16): w0k = lno( Dk ) (15) p( σi | Dk ) wik = ln (16) p( σi | Dk ) For the patient that is diagnosed it is analyzed the list of symptoms and it is calcu- lated for each symptom σi the value of the function signum, using the expression (17): ⎧− 1,if σi = 0 sign( σi ) = ⎨ , i = 1,. B Artificial Neural Networks There are a lot of cases when is not possible to implement human intelligence with expert systems. The initial idea was that in order to reproduce human intelligence, it would be necessary to build systems with a similar architecture [6]. Artificial neural networks are developed based on brain structure, representing a simplified mathematical model of central nervous system. Like the brain, artificial neural networks can recognize patterns, manage data, and, most important, learn [7]. They are made by artificial neurons, which implement the essence of biologi- cal neuron. In this system, artificial neural networks are used in order to make some predic- tions regarding the treatment response for a patient infected with hepatitis C virus. Hepatitis C is a serious and frequent disease and its evolution has to be carefully overseen during the treatment. Even the efficiency of the hepatitis C treatment improves continuously, the burden of this infection will remain a major issue for the next several decades. The system offers for each evaluated biological indicator predictions regarding the next 12 months evolution, indicating its growing tendency, its stabilizing or de- creasing tendency. It was developed using feed-forward neural networks with back-propagation learning algorithm. Each neural network has a layer of 10 hidden neurons, a single output unit and a variable number of inputs. For each of the four biological indicators that have been studied, there are four layers of neural networks. The networks on the first layer receive as inputs: pa- tient’s age, sex, location (rural/urban), treatment scheme, Knodell score, hepatic fibrosis score and value of the parameter for which the prediction is made, at the initial moment (before the treatment starts). Medical Predictions System works have the same structure as the first layer ones, but they have in addition, as inputs, the outputs of the networks on the former layers; therefore, the networks on the last layer will have not 7 inputs (as the networks on the first layer) but 10 (the initial inputs and the values of biological indicators at 3, 6, and 9 months). The advantage of this architecture is that the input data are processed separate for each biological indicator. The disadvantage is that the errors are propagated through the system because the results of the networks from the first level (to- gether with their errors) are used in the following levels. It develops a multifunctional database and imple- ments an expert system used in order to diagnose different types of hepatitis and to realize some predictions regarding the evolution of the patient and the response to the treatment. The system uses two major components (an inference machine and an architecture of neural networks) that operate on the multifunctional data- base (Fig.

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Standards of care are determined on the basis of all clinical data available for an individual case and are subject to change as scientific knowledge and technology advance and patterns of care evolve cheap malegra fxt plus 160 mg without a prescription. Adherence to guideline recommendations will not ensure a successful outcome in every case purchase generic malegra fxt plus on line, nor should they be construed as including all proper methods of care or excluding other acceptable methods of care aimed at the same results purchase malegra fxt plus in united states online. The ultimate judgement must be made by the appropriate healthcare professional(s) responsible for clinical decisions regarding a particular clinical procedure or treatment plan. This judgement should only be arrived at following discussion of the options with the patient, covering the diagnostic and treatment choices available. It is advised, however, that significant departures from the national guideline or any local guidelines derived from it should be fully documented in the patient’s case notes at the time the relevant decision is taken. Some recommendations may be for medicines prescribed outwith the marketing authorisation (product licence). It is not unusual for medicines to be prescribed outwith their product licence and this can be necessary for a variety of reasons. Generally the unlicensed use of medicines becomes necessary if the clinical need cannot be met by licensed medicines; such use should be supported by appropriate evidence and experience. The prescriber should be able to justify and feel competent in using such medicines. Prior to prescribing, the licensing status of a medication should be checked in the current version of the british National formulary (bNf). The grade of recommendation relates to the strength of the supporting evidence on which the evidence is based. B anticholinergic drugs should not be used as first line treatment in patients with Parkinson’s disease. These were: communication attitudes to drug therapy information needs family/carer needs non-motor symptoms multidisciplinary team working. These topics reflect the most frequently cited issues and are not a comprehensive list of insights generated by qualitative researchers. A sharing of information with family members was perceived to be vital for each person to understand their individual situation. In addition, interruption, or the finishing of sentences by others, was highlighted as impacting on a person’s ability to interact in a social context. This may lead to social isolation as the person may be embarrassed by their disease and its symptoms. The effect of altered emotions during the communication process was also highlighted. One person said “the people you work with do not understand when I have to ask to leave early on Tuesday to go to [my] appointment”. The main issue identified was the importance of information provision at the time of diagnosis about the condition, therapy and progression. One factor that impacted on the experience of taking medication was for people to realise that they were not alone. Nevertheless, it gives an indication of the level of distress that can follow from an information deficit. It is important the information is appropriately targeted and at the correct educational level to ensure complete understanding. A particularly important time for giving information and education is at the time of diagnosis. Some skill has to be exercised in determining the amount of information imparted at diagnosis, steering between too little - “I was shocked in maybe 12 minutes of his total time seeing me, he diagnosed me with an illness and gave me no hope and told me to take some medicine, period. And then he dismissed me”10 - and too much “knowing all the facts would probably have finished me off”. They should also make sure that patients are aware of the potential risks arising from unreliable, inaccurate and unregulated sources of information about their condition such as the Internet and newspapers. This could lead to possible negative impact on the maintenance and strength of relationships with their clinical advisors - “when I was diagnosed, I remember just kind of crying that day and coming home and looking up that word in the dictionary and it was like, well, I’m going to die with this disease”. Many patients reported them useful for reinforcing such factors as medicines management or dietary requirements, but a number used coping strategies which centred upon maintaining as normal a life as possible and found the self help group a source of some discomfort - “I have seen the future in the eyes, faces and activities, or inactivities, of my fellow Parkinson sufferers”. Carers have unique and individualised coping strategies for dealing with the daily pressures of care. Many carers report the difficulty in adopting the twin roles of therapist and friend. The evidence reports that spouses often found great difficulty in watching their partner struggle and can be frustrated by the illness without promoting dependency but helping when necessary. Sometimes I haven’t come to grips with it because I think I should have done it rather than him because it was hard on him. This highlights the need for more appropriate qualitative research from the uk in this area. In two studies carried out by the same researchers the main impact of non-motor symptoms was perceived to be psychosocial - “embarrassed people just keep staring at you when you cannot get your words out… so I just avoid people”. In speech, in contrast to straightforward articulation difficulties, patients identified issues such as distractibility, diminished attention span, and difficulty finding words and formulating ideas - “It’s difficult to keep my attention going, I drift away. Whilst Parkinson’s disease is the commonest cause of a parkinsonian syndrome, there are several other degenerative and non-degenerative diseases that can mimic it (see Table 1). Accurate diagnosis is essential to ensure that patients receive the correct information and treatment. Table 1: Common mimics of Parkinson’s disease Degenerative disorders non-degenerative disorders Multiple system atrophy essential tremor Progressive supranuclear palsy Dystonic tremor Corticobasal degeneration Cerebrovascular disease Dementia with lewy bodies Drug-induced parkinsonism Alzheimer’s disease 4. This diagnosis requires clinical skill but is open to a degree of subjectivity and error. It is important to consider the accuracy of the clinical diagnosis against a suitable reference standard, which for almost all cases of Parkinson’s disease remains neuropathological confirmation at post mortem (a very small percentage of cases can be diagnosed genetically, see section 4. Thus, it is important to consider the accuracy of the clinical diagnosis both in the early stage of the disease when decisions about initiating treatment will be made and also later in the disease. These flaws included: the reference standard was only available in a limited spectrum of patients that did not reflect 2+ the types of patients seen in most clinical settings, particularly in the initial stages of the disease;18-21 the patients included were younger (mean age 53-65 years) with longer disease duration than seen in many clinical settings; details of how the clinical diagnosis was made were not available; the clinicians were often highly specialised movement disorder experts;19 clinical diagnoses were identified by retrospective review of the case notes after death, which may have reduced accuracy;18,19 and one study did not blind the clinical diagnosis to the pathological diagnosis. The sensitivity and especially specificity of expert clinical diagnosis increased with follow up and the final 2+ clinical diagnosis had a good sensitivity (range 0. This should include a review of the ongoing benefits in those started on dopamine replacement therapy. There are two commonly used research criteria, the uk Parkinson’s Disease Society brain bank criteria22 and the Gelb criteria23 (see Annex 2). Improved diagnostic accuracy would be most useful early in the course of parkinsonian disorders when clinical diagnosis is most inaccurate and important management decisions must be made. The Gelb criteria for probable Parkinson’s disease require at least three years follow up from symptom onset. Only one small study (n=100) from the uk has assessed the accuracy of using the uk brain bank and Gelb criteria late in the disease compared to neuropathological confirmation of the diagnosis. No direct comparison of research criteria versus expert clinical diagnosis was possible apart from the positive predictive value, which was similar (0. Indirect comparison with studies that compared the final expert clinical diagnosis with post mortem diagnosis suggests that expert clinical diagnosis has a higher specificity than research criteria.

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