Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Fuzzy-based PROMETHEE method for performance ranking of SARS-CoV-2 IgM antibody tests

This article belongs to the Special Issue Monitoring and Detection for SARS-CoV-2 and Its Variants.Antibody tests, widely used as a complementary approach to reverse transcriptase-polymerase chain reaction testing in identifying COVID-19 cases, are used to measure antibodies developed for COVID-19. This study aimed to evaluate the different parameters of the FDA-authorized SARS-CoV-2 IgM antibody tests and to rank them according to their performance levels. In the study, we involved 27 antibody tests, and the analyzes were performed using the fuzzy preference ranking organization method for the enrichment evaluation model, a multi-criteria decision-making model. While criteria such as analytical sensitivity, specificity, positive predictive value, and negative predictive value were evaluated in the study, the ranking was reported by determining the importance levels of the criteria. According to our evaluation, Innovita 2019-nCoV Ab Test (colloidal gold) was at the top of the ranking. While Cellex qSARS-CoV-2 IgG/IgM Rapid Test and Assure COVID-19 IgG/IgM Rapid Tester ranked second and third on the list, the InBios-SCoV 2 Detect Ig M ELISA Rapid Test Kit was determined as the least preferable. The fuzzy preference ranking organization method for enrichment evaluation, which has been applied to many fields, can help decision-makers choose the appropriate antibody test for managing COVID-19 in controlling the global pandemic

A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases

The largest vertebrate viruses known, infecting humans, and other vertebrates are poxviruses including cowpox, vaccinia, variola (smallpox), and monkeypox viruses. Monkeypox was limited to the rain forests of central and western Africa until 2003. A smallpox-like viral infection caused by a virus of zoonotic origin, monkeypox belongs to the genus Orthopoxvirus, family Poxviridae, and sub-family Chordopoxvirinae. Monkeypox has a clinical presentation like ordinary forms of smallpox, including flulike symptoms, fever, malaise, back pain, headache, and characteristic rash. In view of the eradication of smallpox, such symptoms in a monkepox endemic region should be carefully diagnosed. The problem in diagnosing monkeypox lies in the fact that it is clinically indistinguishable from other pox-like illnesses making virus differentiation difficult. In this paper, we present a neuro-fuzzy based model for early diagnosis of monkeypox virus with a differentiation from other pox families

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Study on Viral Transmission Impact on Human Population Using Fractional Order Zika Virus Model

This work comprises the spread of Zika virus between humans and mosquitoes as a mathematical simulation under fractional order, which also incorporates the asymptotically infected human population. For determining the solution of the model the fuzzy Laplace transform technique is utilized. By combining fuzzy logic with the Laplace transform, we can analyze systems even when we lack precise information. Further, the sensitivity analysis is performed to validate the model. On top of that the population dynamics of both human and mosquito populations are discussed using numerical data and the graphical result of the model is presented. The main objective of this work is to study the dynamics of the Zika virus and to examine the effect of virus on humans when the transmission occurs between humans and from mosquitoes, under fractional order. The outcome of these comparisons suggests that even by reducing a minute fractional part of transmission through mosquitoes results in a greater reduction of Zika exposed population. The comparisons improve the understanding of fractional level transmission resulting in more effective drug administration to patients. The Hyers-Ulam stability method is a mathematical technique used to study the stability of functional equations. Eventually, Ulam Hyers and Ulam Hyers Rassias stability are employed to assess the stability of the proposed model

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Detecting Sepsis Using Sepsis-Related Organ Failure Assessment (SOFA) and an Electronic Sepsis Prompt in Intensive Care Unit Adult Patients

Sepsis is an elusive and costly syndrome that is one of the leading causes of death globally. Annually, there are approximately 19 million cases of sepsis that result in more than 5 million deaths. The Agency for Healthcare Research and Quality (AHRQ) ranked sepsis as the most expensive condition ($23.7 billion) for patients treated in hospitals in the United States (U.S.). Nurses are critical in the early identification of sepsis and implementation of therapeutic interventions known as the “sepsis bundle”. Previously, sepsis was described as a systemic, pro-inflammatory response to an infection. Sepsis was defined as two or more systemic inflammatory response syndrome (SIRS) criteria with a suspected infection, severe sepsis was defined as sepsis with organ failure and septic shock was defined as severe sepsis with shock. For several decades SIRS criteria with organ failure criteria have been used to develop measurement systems for detection of sepsis. A recent study comparing SIRS criteria to the sepsis-related organ failure assessment (SOFA) score demonstrated that SOFA had greater prognostic accuracy of mortality in patients with an infection than SIRS. This led to sepsis definition changes in 2016. The term “severe sepsis” was dropped and sepsis was defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection leading to tissue injury and organ failure. Many clinicians were concerned that this new definition might lead to late detection of sepsis. What was unknown was how well SIRS with organ failure criteria compared with SOFA in detection of sepsis. Many clinicians in the U.S. working in a TeleICU had been using SIRS with organ failure criteria to support early identification of sepsis. Using human factors science concepts, their practice was studied and an electronic sepsis alert (sepsis prompt) was developed. Thus, the overall objective of this dissertation was to conduct a retrospective study using a large U.S. data repository to determine if an electronic prompt, that uses SIRS and organ failure (OF) criteria, can detect sepsis. Another objective of this study was to determine the prognostic accuracy of the SOFA score and the sepsis prompt in discriminating in-hospital mortality among patients with sepsis in the intensive care unit. Among 2,020,489 patients admitted to ICUs associated with a TeleICU from January 1, 2010, to December 31, 2015, at 459 hospitals throughout the U.S., we identified 912,509 (45%) eligible patients at 183 hospitals. We compared the performance of the SOFA score and sepsis prompt criteria in detecting sepsis. Of those in the primary cohort, a secondary cohort was derived based on presence of sepsis resulting 186,870 (20.5%) patients. To assess performances of the SOFA score and the sepsis prompt (a Fuzzy Logic SIRS and OF algorithm) to detect sepsis, we calculated diagnostic performance of an increase in the SOFA score of 2 or more and criteria met for the Fuzzy Logic SIRS and OF algorithm. For predictive validity, training of baseline risk models was performed on training sets with prediction and performance analytics completed on test sets for each cohort for the outcomes of mortality and sepsis. Results were expressed as the fold change in outcome over deciles of baseline risk of death or risk of sepsis, area under the receiver operating characteristic curve (AUROC), and sensitivity, specificity, and negative and positive predictive values. In the primary cohort (912,509) there were 86,219 (9.4%) who did not survive their hospital stay and 186,870 (20.5%) with suspected sepsis of whom 34,617 (18.5%) did not survive hospitalization. The Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.66-0.67 and adjusted AUROC 0.77, 99% CI: 0.77-0.77) outperformed SOFA (crude AUROC 0.61, 99% CI: 0.61-0.61 and adjusted AUROC 0.74, 99% CI: 0.74-0.74) in discrimination of sepsis in both crude and adjusted AUROC (in-between differences AUROC 0.06; z-value 49.06 and AUROC 0.03; z-value 36.22, respectively). In the primary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.67-0.68 and adjusted AUROC 0.78, 99% CI: 0.77-0.78) outperformed SOFA (crude AUROC 0.64, 99% CI: 0.64-0.64 and adjusted AUROC 0.76, 99% CI: 0.76-0.76) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.03; z-value 24.68 and AUROC 0.02; z-value 14.74, respectively). In the secondary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.57, 99% CI: 0.57-0.58 and adjusted AUROC 0.69, 99% CI: 0.68-0.70) outperformed SOFA (crude AUROC 0.56, 99% CI: 0.56-0.56 and adjusted AUROC 0.68, 99% CI: 0.67-0.68) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.01; z-value 6.86 and AUROC 0.01; z-value 7.53, respectively). The results of this study demonstrated that among adult ICU patients, the predictive validity for sepsis and in-hospital mortality of a complex algorithm based on Fuzzy Logic applied to expanded SIRS criteria with organ failure criteria was better than SOFA for detection of sepsis and for prognostic accuracy of mortality. The findings of this study support the use of a computer-enhanced algorithm that includes a combination of expanded SIRS with organ failure criteria as a tool to assist nurses and healthcare providers in early identification of sepsis

An intelligent decision support system to prevent and control of dengue

Prevention and control of dengue fever are considered as a complex problem in day-to-day life. Noticeable changes in the human population growth, life style, and climate would cause more dengue outbreak in all over the world. The Government of India has developed a number of prevention and control strategies to protect individuals from dengue fever. Though, the strategies provided by the government are not identified based on people, space and time. In order to overcome this issue, the proposed approach presents various alternatives such as vaccination, disease surveillance, vector control, proper sanitation and increased accessed to safe drinking water, strengthening public health activities, awareness creation, and improving nutrition foods for women and child. The proposed alternatives are selected based on people, space and time criteria’s such as low temperature and heavy rain, high mean temperature and high humidity, water accumulation and rainfall resources and facilities, social culture variable and social demographic variable. The selection of alternatives based on multiple criteria’s is considered as a complex problem in decision-making framework. In general, decision makers and administrators are often used linguistic terms to give their opinions. This paper uses fuzzy logic based VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to analyze the linguistic terms collected from the decision makers and rank the best alternatives based on multiple criteria’s

Predicting infections using computational intelligence – A systematic review

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.publishedVersio