A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood

Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand

A consistent neuro-fuzzy inference system

Велики број аутора сматра да велике могућности експертских система леже у хибридним моделима, што су ови системи и доказали у пракси. Мотивисан тиме, предложени модел система у основи представља интеграцију неуронских мрежа и фази система, чиме се боље користе добре стране оба приступа. Полазна основа овог рада је да понашање система, кроз скуп лингвистичких правила, треба да описују управо они који систем највише познају и разумеју (насупрот аутоматски генерисаним правилима која су најчешће рогобатна и неразумљива). Знање експерата из било које области лако се може формулисати вербалним исказима, а теорија фази скупова и фази логике омогућава превођење оваквих исказа у одговарaјуће математичке изразе. Класична теорија фази скупова не задовољава све Булове аксиоме. Из овог разлога у раду је примењена конзистентна реално-вредносна [0,1] логика, која се заснива на интерполативној Буловој алгебри (ИБА). Свака логичка функција може се једнозначно трансформисати у одговарајући генерализовани Булов полином (ГБП) коришћењем ИБА при чему се чувају сви Булови закони. Оправданост коришћења конзистентног приступа најпре је илустрована на примеру конзистентног фази система закључивања (КФИС). Сврха приказаног КФИС-а је да процени могућност да је пацијент на дијализи трбушне марамице (лат. peritoneum) оболео од перитонитиса. Добијени резултати указују на чињеницу да класичан ФИС и конзистентан приступ не воде увек ка истим резултатима, а разлика је најуочљивија када правила укључују негацију. Како би се КФИС даље унапредио, коришћена је неуронска мрежа, тј. њен алгоритам учења, који, на основу скупа улазно-излазних података, подешава параметре тако да више одговарају реалном систему. На тај начин, предложени конзистентан неуро-фази систем (КНФИС) користи знање садржано у подацима и унапређује закључивање. Такође, елиминише се субјективност коју експерти у некој мери изражавају приликом дефинисања параметара система...A number of authors find that the greatest potential of expert systems lies in hybrid models, and such models have proven this viewpoint in practice.Therein lies the motivation for introducing a new system model, integrating neural networks and fuzzy systems, thus building on the best features of each of these approaches. The main premise of this thesis is that the behavior of a system should be described, through a set of linguistic rules, by those who know and understand the system the best (as opposed to the automatic generation of rules that are often cumbersome and incomprehensible). Expert knowledge in any domain can be easily expressed in the form of verbal statements, and fuzzy set theory and fuzzy logic enable the transformation of such verbal statements into mathematical expressions. Conventional fuzzy set theory does not satisfy all Boolean axioms. For this reason, the consistent real-valued [0,1] logic, based on the Interpolative realization of Boolean algebra (IBA), is applied in this thesis. Any logical function can be uniquely transformed into a corresponding generalized Boolean polynomial (GBP) using IBA thereby preserving all Boolean laws. The justification for using a consistent approach is first illustrated on an example of a consistent fuzzy inference system (CFIS). The purpose of the described CFIS is to estimate the likelihood that a patient undergoing peritoneal dialysis, has peritonitis. The obtained results demonstrate that conventional FIS and the Boolean consistent approach do not always lead to the same results, and this discrepancy is most pronounced when the established rules include negations. In order to further enhance CFIS a neural network, or, more precisely, its learning algorithm, is used to fine-tune the parameters, in accordance with a set of input-output data, so that the parameters better suit the real system. Consequently, the proposed consistent neuro-fuzzy system (CNFIS) uses the knowledge contained in the data to improve the inference process. In addition, it eliminates the subjectivity incorporated into the system by experts when defining the parameters of the system..

The evaluation and enhancement of case driven diagnostic advice systems: a study in three domains

Relevant literature has been reviewed regarding the performance, implementation and evaluation of computer based medical decision support systems. The diagnostic performance of five simple case driven acute chest pain advice systems, have been compared using a standardized set of clinical records. A Bayesian inference model demonstrated superiority over two derived by logistic regression. Small data set flow charts performed well but both relied upon the use of expert opinion. A Bayesian acute abdominal pain diagnostic advice system has been evaluated in a clinical trial. Standardized data collection improved the diagnostic performance of doctors. In practice, the computer system offered little additional user benefit. From further tests in primary care, it was concluded that, whereas general practitioners might enhance their performance by using data collection sheets, paramedics might benefit through direct use of the computer. DERMIS is a new dermatology primary care diagnostic advice system. Components include a database derived from 5203 prospectively collected clinical records, a user interface, and an enhanced Bayesian inference model incorporating combined frequency estimates, expert beliefs and rationalized end-point groups. On laboratory testing, the diagnostic accuracy of DERMIS was 83%. The correct diagnosis appeared in the top three, of a possible 42 disease list on 97% of occasions. In a semi-field trial of DERMIS involving 49 general practitioners, doctors did not always collect the same information as a dermatologist but were able to significantly increase their chance of making a correct diagnosis through use of the computer system. It has been concluded that although implementation of DERMIS might well increase general practitioner diagnostic accuracy and lead to improvements in the management of skin disease in primary care, rates of referral for specialist opinion might not be affected unless standard management plans are adopted

Adaptive fuzzy system for algorithmic trading : interpolative Boolean approach

Тема овог рада je адаптивни фази систем за алгоритамско трговање. Систем је развијен коришћењем интерполативног Буловог приступа фази моделовању, анализи података и управљању. Предложени приступ укључује интерполативне логичке моделе за фази препознавање ценовних образаца на тржишту, логички ДуПонт метод за аутоматизовану анализу профитабилности предузећа, интерполативни фази контролер за управљање трговањем и генетски алгоритам за обучавање интерполативног фази контролера ради откривања стратегија. Интерполативни Булов приступ, заснован на интерполативној Буловој алгебри, превазилази проблем неконзистентности фази логике. Конструисани адаптивни фази систем може самостално, из података, да открије успешне стратегије, примени их за алгоритамско трговање и адаптира у случају пада њихових перформанси. Успешност система тестирана је на подацима са америчког тржишта акција, међународног девизног тржишта и тржишта криптовалута.The topic of this thesis is adaptive fuzzy system for algorithmic trading. The system is developed using interpolative Boolean approach for fuzzy modeling, data analysis and control. The proposed approach includes interpolative logical models for fuzzy recognition of price patterns in market data, logical DuPont method for automated analysis of company’s profitability, interpolative fuzzy controller for trading and a genetic algorithm for extracting trading strategies by training interpolative fuzzy controller. Interpolative Boolean approach, based on interpolative Boolean agebra, solves the problem of fuzzy logic’s inconsistency with Boolean axioms. The proposed system can independently discover successful trading strategies from data, apply them for algorithmic trading and adapt in the case of performance deterioration. The system was tested on historical data from US equity, foreign exchange market and cryptocurrency market

Decision-making strategies in the general practice

With regard to the question what is to be tran$mitted in teaching two aspects have to be considered: a) the contents of the specialty; and b) the problem-solving methods with regard to the specialty. On the first aspect an overwhelming amoUnt of book$ and articles has bee~ written in medicine. The second 8Spect is usually only mentioned in passing. In my opinion, ~his subject has thusfar been greatly undervalued. When one is unable to trace the - problem-solving processes how can anyone determine the efficscy, the effectivity, and the efficiency of this process, or value~ the outcome. To state it in Magerien terms: "If you do not know where to g;o, you may very well end up somewhere else- and not even know it." How physicians solve clinical problems is the main object of this research. The investigator studied and modeled two of the eldest and famous ways of pJroblei!Jlrsolving: the deductive and the inductive strategy9 with the modern probability reasoning viewed as an extension of the latter strategy. All 68 physicians who participated in this investigation used the inductive strategy for the -usually four - presented patient-problems. Within the inductive strategy three variants could be distinguished. The consequences of this finding are far-reaching. As the inductive strategy does not include a logical hierarchy of argumentationsteps, retracing of the process is impossible. (This aspect is also relate~ to our opinions about experience-knowledge end teaching)A As the hypothesis generation is prior to the acquisition of infot~tion, this latter aspect can only be viewed in the light of the former~ and thus limited to a small number of domains. As the hypothesis generation is - partly - unrelated to the total available amount of information, the decision making (chopsing the ultimate diagnostic hypothesis) will usually follow implicit~ personal trends and standards, e.g. satisfying minimal eicpectations (Satisficing Theory, Simon} or risk-avoiding prospects (Prospect Theory, Kahnemann & Tversky). It suggests a highly personal character of diagnostics and/or the therapeutic management, which is contradictory to general accessibility of medical knowledge and medical teaching. This feature of personal concepts easily links up with Polanyi's theory of "PeJrsonal" or 91Tacit Knowledge" as contrasted to "Objective Knowledge" (Popper) 9 which has general accessibility and validity. During the investigation this as~oct came forth. The framework of the investigation (patient simulation) end the special definitions and coding of illness elements (symptoms, signs9 tests) all~wed for comparing similar conceptions (diagnoses, treatments) ~ong the participants. These comparisons confirm Polanyi's theory and the concepts of inductive reasoning. Mutual comparibility of diagnoses seems hardly possible when analysing these conceptions into their basic elements (symptoms etc.). This aspect touches upon one of the main cornerstones of clinical ~edicine. When the starting positions have not been unequivocally defined treatment, .,;ie-Jed as the intervention in the natural course of a disease, can only lead t4:!1 uncertain outcomes. The lack of · standardized :medical definitions and a tmiform, unambiguous taxonomy inhibits the application of a formalised, normative decision theorry for clinical medicine. Future planning aims at a reconsideration of medical terminology~ medical taxonomy and medical problem-solving methods by means of clustering the basic elements of clinical medicine

Clinical Reasoning and Causal Attribution in Medical Diagnosis

Forming a medical diagnosis is a complicated reasoning process undertaken by physicians. Although there has been much research focusing on clinical reasoning approaches, there is limited empirical evidence in relation to causal attribution in medical diagnosis. The research on which this thesis is based explored and examined the social process of medical diagnosis and provides an explanation of the clinical reasoning and causal attribution used by physicians. The research was undertaken in an Emergency Department within an acute hospital, the data were collected using mixed method approach including one to one semi-structured interviews with individual physicians; observation of their medical assessments of patients and secondary data analysis of the subsequent recorded medical notes. The study involved 202 patients and 26 physicians. The analysis of the physicians’ semi-structured interviews, shows how physicians describe the diagnostic step process and how they blend their clinical reasoning skills and professional judgment with evidence-based medicine. Physicians apply prior learning of taught biomedical and pathophysiological knowledge to question patients using pattern recognition of common signs and symptoms of disease. These findings are portrayed through taped narratives of the physician/patient interaction during the medical diagnostic process, which shows how physicians control the medical encounter. The analysis/interpretation of documentary evidence (recorded medical notes) provides an insight into the way in which physicians used the information gathered during the diagnostic step process. By using SPSS it was possible to cluster the cases (individual patients) into groups. This stage-ordered classification procedure demonstrated commonality amongst individual cases whilst highlighting the uniqueness of any cases. A pattern emerged of two groups of cases: Group 1 - comprised of patients with the presenting complaints of chest pain, shortness of breath, collapse, abdominal pain, per rectal bleed, nausea, vascular and neurological problems and Group 2 - comprised of patients presenting with trauma, mechanical falls, miscarriage/gynaecological problems, allergies/rashes and dental problems. Findings show that the clinical reasoning approaches used varied according to the complexity of the patient’s presenting complaint. The recorded medical notes for the patients in Group 1, were comprehensive and demonstrated a combined approach of hypothetic-deductive and probabilistic reasoning which enabled the physicians to deal with the degree of uncertainty that is inherent in medicine. The recorded process in the medical notes was shortened for the majority of patients in Group 2, and here the clinical reasoning approach used was found to deterministic. It is acknowledged, that this is not always the case. By using crisp set QCA it was possible to explore causal conditions consistent with Group 1. Further analysis led to examination of the link of causal conditions presented in the medical notes with the individual impression/working diagnosis made by physicians.Plymouth Universit

Evaluation of strategies for reducing the burden of COPD in the UK using Bayesian methods

Chronic obstructive pulmonary disease (COPD) is responsible for 5.3% of all deaths and 1.7% of all hospital admissions in the UK. This thesis focuses on strategies to reduce COPD burden by targeting three aspects across the public healthcare system: prevention, emergency treatment, and long-term management. Analyses were performed in a Bayesian framework to exploit its flexibility in modelling uncertainty and the incorporation of prior knowledge. First, I assessed whether communication of personalised disease risk in primary care is an effective smoking cessation intervention, using cost-effectiveness and value of information analyses based on various data sources across the literature. The odds ratio for the effectiveness of communication of personalised disease risk was 1.48 (95%CrI:0.91-2.26). While I found a probability of cost-effectiveness of about 90%, further research up to a maximum of £27 million is justified to reduce the uncertainty around this estimate. Secondly, I assessed whether case ascertainment affects the detection of poorly performing hospital trusts in the treatment of acute exacerbation of COPD (AECOPD) in secondary care, using data from the National Asthma and COPD Audit Programme. Case ascertainment was associated with 30-day mortality (OR:1.74; 1.25-2.41) and adjusting for it impacted the findings, with 5 trusts becoming outliers and 2 trusts no longer classified as outliers. Finally, using general practice data from Clinical Practice Research Datalink, I assessed whether new guidelines suggesting triple therapy (long-acting beta-2 agonists, LABA + long-acting muscarinic antagonists, LAMA + inhaled corticosteroids, ICS) for the treatment of those with poorly-controlled COPD on LABA+LAMA dual therapy improves disease outcomes. Triple therapy was not associated with severe AECOPD (IRR:1.00; 0.93-1.07) or mortality (IRR:0.95; 0.86-1.06), but was associated with increased risk of pneumonia (IRR:1.19; 1.05-1.35). This thesis applied sophisticated Bayesian methods to increase understanding of how COPD burden could be reduced in different areas of the public healthcare system.Open Acces

Collected Papers (on Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics), Volume X

This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬