Knowledge Based Expert System for Diagnosis of COVID-19

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Expert System KORDEX for Solving of Urgent Cardiology Problems

The article provides an analysis of the reasons for the need to develop an expert system in emergency cardiology. The principles of development and reasons for modification of the KORDEX expert system which is used for the myocardial infarction prognosis are described. The method of comparative estimation of parameters used to create a knowledge base is considered. Examples of expert rules, including rules that take into account the postponed COVID-19 are shown. The debugging of the expert system and the results of its use in practical medicine are described. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Review of the Application of Artificial Intelligence in Medicine: From Data to Personalised Models

Artificial intelligence leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers decision making of clinicians. Starting from data (medical images, biomarkers, patients' data) and using powerful tools such as convolutional neural networks, classification, and regression models etc., it aims at creating personalized models, adapted to each patient, which can be applied in real clinical practice as a decision support system to doctors. This chapter discusses the use of AI in medicine, with an emphasis on the classification of patients with carotid artery disease, evaluation of patient conditions with familiar cardiomyopathy, and COVID-19 models (personalized and epidemiological). The chapter also discusses model integration into a cloud-based platform to deal with model testing without any special software needs. Although AI has great potential in the medical field, the sociological and ethical complexity of these applications necessitates additional analysis, evidence of their medical efficacy, economic worth, and the creation of multidisciplinary methods for their wider deployment in clinical practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Expert System for the Timely Diagnosis of Infectious Diseases

Infectious diseases such as COVID-19, dengue, diphtheria, etc., are spreading throughout the world, just like in Peru. Some of the symptoms of these diseases are similar. Rapid diagnosis is not available in this case, especially in regions with limited medical facilities. In addition, some people are unaware of the symptoms caused by these diseases. The objective of the research is to design a rule-based web expert system prototype using the Buchanan and Rational Unified Process (RUP) methodology and evaluate to determine the feasibility of building the system for early detection and timely treatment of infectious diseases such as COVID-19, Zika, Dengue, Diphtheria, Influenza, Chikungunya, and Monkeypox. The result was a prototype of an expert system with friendly and easy-to-use interfaces. In addition, the quality level of the prototype was evaluated through expert judgment, who analyzed the system's efficiency, usability, security, and functionality. After calculating the scores of the evaluated criteria, the total average was 4.74 out of 3, which is the minimum average for the feasibility of the expert system to be considered acceptable, considering the level of quality. In conclusion, it was possible to design and evaluate the expert system prototype. In addition, the results also show that it is feasible to build the proposed system for the early detection and treatment of infectious diseases for the benefit of people's health. © 2023 Seventh Sense Research Group®

Artificial Intelligence-Based Predictive Tools for Life-Threatening Diseases

The large-scale outbreaks of infectious pandemic diseases emerged regularly throughout history and created notable economic, social, and political disruptions. Major pandemics affect a wide geographic area significantly increasing morbidity and mortality. The world has come across numerous remarkable pandemics such as the Black Death, measles, smallpox, influenza, plague, cholera, Spanish flu, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and Ebola virus and is now combating the new coronavirus disease 2019 (COVID-19) pandemic affecting humanity greatly. Studies suggest that the likelihood of pandemic threats is due to the diversity of pathogens, changes in the dynamics of disease transmission and severity, human-pathogen interaction, increased globalization, urbanization, huge exploitation of land and natural resources, and global warming. The pandemic risk burden poses serious challenges to humanity and these trends will prolong and intensify over time. For the well-being of humanity, administration of public health measures, techniques to intercept and control infection, pharmaceutical intervention, global surveillance programs, novel technologies to identify disease biomarkers, and vaccine production prove to be effective beneficiary responses to identify and limit emerging outbreaks and to escalate preparedness and health capacity. The extensive amount of data produced during the pandemic has given a lot of chances to the researchers and healthcare providers to evaluate new trends, detect vulnerable groups, and solve long-standing issues in the healthcare industry. The healthcare industry has sought to use the most comprehensive data and predictive analytics software tools employing intelligent data technology, artificial intelligence (AI), machine learning (ML), and deep learning (DL) and has leveraged to gain insight, establish innovative ways to ease sustainable demand and supply, and pitch straight into the prospective benefits to foster the fight against the pandemic. Hence, these predictive models can support hospitals, healthcare settings, state health organizations, and government establishments to speculate the influence of COVID-19 and prepare for the future. In this chapter, a comprehensive investigation of various data analytic tools that are used in expert systems, proposed for pandemic and epidemic diseases, is discussed. The key issues, challenges, and opportunities of the existing and current methods are also discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm.

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee's physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.

Diagnosis of Skin Cancer by Using Fuzzy-Ann Expert System with Unification of Improved Gini Index Random Forest-Based Feature

In addition to being one of the most widespread and lethal diseases in the world, skin cancer is also one of the most common types of cancer. However, due to its complexity and fuzzy nature, the clinical diagnosis process of any disease, including skin cancer, prostate cancer, coronary artery disorders, diabetes, and COVID-19, is frequently accompanied by doubt. In order to address the uncertainty and ambiguity surrounding the diagnosis of skin cancer as well as the heavier burden on the overlay of the network nodes of the fuzzy neural network system that frequently occurs due to insignificant features that are used to predict or diagnose the disease, a fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm was proposed in this work. A Greater Gini Index Out of the 30 features in the dataset, the five most fitting features of the diagnostic Wisconsin breast cancer database were chosen using a random forest-based feature importance measure algorithm. Two sets of classification models were created using the logistic regression, support vector machine, k-nearest neighbour, random forest, and Gaussian naive Bayes learning algorithms. As a result, models for classification that included all features (30) and models that only used the top five features were used. The efficacy of the two sets of categorization models was assessed, and the results of the assessment were compared. The comparison's results show that the models with the fittest features outperformed those with the most complete features in terms of accuracy, sensitivity, and sensitivity. A fuzzy neural network-based expert system was therefore developed, utilising the five best features, and it achieved 99.83 percent accuracy, 99.86 percent sensitivity, and 99.64 percent specificity. The system built in this study also stands to be the best in terms of accuracy, sensitivity, and specificity when compared to prior research that used fuzzy neural networks or other applicable artificial intelligence techniques on the same dataset for the diagnosis of skin cancer. The z-test was also performed, and the test result demonstrates that the system has significantly improved accuracy for early skin cancer diagnosis. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Fusion Based Self Diagnosis Expert System Empowered with Leven-berg Marquardt Algorithm

COVID-19 coronavirus disease is the latest virus in the new century. The World Health Organization- WHO organization announced that COVID-19 disease is a pandemic that leads to thousands of death in short time of spam. A quick and accurate diagnosis of COVID-19 shows an important role in its prevention. This study is based on a fusion-based Self-Diagnosis Expert System Empowered by the Leven-berg Marquardt Algorithm for the diagnosis of diseases. Leven-berg Marquardt has been implemented for the classification of different symptoms of the diseases and relates the results for their diagnosis. The MatLab software was used for the simulation purpose. The proposed fusion-based LB increased the accuracy in the training and validation process to be 10 times more efficient than the existing. The fusion technique achieved an overall accuracy of 98.86%, and 99.09% in all performance metrics which included TNR, precision, and FPR statistical parameters. © 2022 IEEE.

A Rule-Based Expert System for Teachers' Certification in the Use of Learning Management Systems

In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers' LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers' LMS competence evidenced in their courses. © 2022, Universidad Internacional de la Rioja. All rights reserved.

Developing a Risk-Stratification Dashboard Based in Artificial Intelligence (AI) to Identify Patients for a Model of Health Care: A Population-Based Cohort Study in Brazil

Objectives: Care coordination is a key component of the population health management. However, the mechanism for identifying patients who may benefit the most from this model of care is unclear. The objective of study is to evaluate the performance of a risk-stratification instrument using a model of AI - Rule-based expert system (RBES) - in predicting healthcare utilization and costs. Method(s): Retrospective cohort study from beneficiaries of a health plan using administrative databases (prior authorizations claims systems): 27,539 individuals were assigned a predicted illness burden score using a case-mix adjustment system from diagnoses and health utilization data (2019 to 2021). Population was stratified according to the score into three main groups: G1) case management;G2) health support;G3) health promotion. Analysis was also performed in subgroups: prolonged hospitalization, readmission, complex medical conditions (CC), continued therapy (CT) (G1);chronic unstable (CU), post-COVID 19, high cost, high user (G2);healthy elderly, risk factor, low risk (G3). Data Science team analyzed population using algorithms which uses a set of logical rules derivatives of human specialists. Result(s): According to score 1,053 individuals stratified in G1, average age 68 years, annual cost U$11,318, 10 times more than average;G2, n=5,429;67 years;U$2,863;G3, n=21,037;53 years;U$246. The sickest population: 3.8%, 19.7% and 76.5% uses about 37%, 48% and 15% of healthcare expenses respectively. Most representative subgroups: CC, CT, and CU with average annual cost five or more times than average. Conclusion(s): Dashboard developed using RBES tools can supports healthcare management. Stratifying risk helps to address specific health care challenges, to align levels of care, to implement a value-based care approach. Also demonstrates to be the most logical and practical initial step to create a data set with labeled variables to start a machine learning using supervised training - the next phase in this project. Copyright © 2022

Prediction System for Diagnosis and Detection of Coronavirus Disease-2019 (COVID-19): A Fuzzy-Soft Expert System

In early December 2019, a new virus named "2019 novel coronavirus (2019-nCoV)" appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the currentwork, we will propose a novel fuzzy softmodal (i.e., fuzzy-soft expert system) for early detection of COVID-19. Themain construction of the fuzzy-soft expert systemconsists of five portions. The exploratory study includes sixty patients (i.e., fortymales and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19 (i.e., shortness of breath, sore throat, cough, fever, and age).We will use the algorithm proposed by Kong et al. to detect these patients who may suffer from COVID-19. In this way, the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not. Finally, we present the comparison between the present system and the fuzzy expert system. © 2023 Tech Science Press. All rights reserved.

Mamdani Fuzzy-Based Assessment of Telework Capability of Philippine Government Employees

Due to the advent of the COVID-19 pandemic, the Philippine government encouraged enterprises and businesses to utilize flexible work arrangements such as work-from-home (WFH) or telecommuting setup. Nowadays, the key components necessary for a telecommuting include a WiFi-enabled IT equipment, secured work environment, and reliable internet connection, while research shows that type of work and computer literacy are also key factors for telework implementation. Multiple studies in relation to telework have already been conducted but some studies were deemed inconclusive and need further analysis. Therefore, in this study, a Mamdani fuzzy-based model was developed for telework capability assessment for Philippine government employees based on four significant factors namely: internet speed, IT equipment availability, computer literacy, and type of work, which are expressed in linguistic representations. The proposed fuzzy system can provide a feedback telework capability score based on the four input parameters which may also be characterized with the potential telecommuting cost requirement.

Expert API for Early Detection of TB Disease with Forward Chaining and Certainty Factor Algorithms

Despite being a curable disease, Tuberculosis has become the leading cause of death of infectious disease prior to COVID-19. It has asymptomatic infections that are hard to detect for weeks or years. Although there have been many studies on Tuberculosis disease detection and prevention, very few of them discuss the creation of an expert system based on API. Hence, in this study we propose an Expert API that implements Forward Chaining and Certainty Factor algorithms for the task of Tuberculosis early detection. The evaluation of the proposed system was carried out using several testing methods and in-depth interviews with medical experts. We got a satisfactory result for this study. © 2022 Slovene Society Informatika. All rights reserved.

RES-Q: Self-evolving Post-disaster Response Plans Utilizing Semantics

During the last decade, communities at local and national level are implementing actions geared towards improving disaster resilience. In this context, the importance of ICT in disaster risk management is rapidly increasing globally, especially nowadays amidst the climate crisis and the covid-19 pandemic. However, disaster risk management operations require contributions and collaboration of different type of actors and infrastructures with different functions, rules, protocols and datasets, forming complex contexts in decision making and event coordination. Hence, semantic interoperability between the various stakeholders is one of the challenges to be confronted. In this paper, we present the RES-Q (RESCUE) approach that proposes an information technology solution concerning the real-time recommendation and orchestration of post-disaster response plans. The implemented RES-Q prototype comprises an expert system and a workflow execution engine based on an ontological infrastructure for modeling the response actions for each type of disaster. The ontological model is designed using a multi-layer approach encapsulating the required knowledge streams and a semantic rule repository. During the execution of a post-disaster plan, the system reasons over the rules and composes the next steps of the corresponding response processes. The rule repository is able to infer new knowledge as each plan progresses, which can update the RES-Q ontology accordingly. © 2022, National Technical University of Athens. All rights reserved.

DESIGNING A MEDICAL EXPERT SYSTEM BASED ON FUZZY LOGIC

During the COVID-19 pandemic, humanity faced various health problems. One of the most common diseases is pneumonia. The life of every person depends on the correct and effective diagnosis of the disease. Currently, a large number of software applications with elements of artificial intelligence are being developed, which can reduce the time of patient care, improve the methodology and efficiency of disease diagnosis. With our research, we strive to contribute to the development of such software applications, namely, to develop software tools with elements of fuzzy logic. To develop a decision-making system, scales and algorithms in order to assess we considered the prognosis of the severity of community-acquired pneumonia PORT(PSI), CURB/CRB-65 and SMART-COP/SMART-CO. To improve the quality of processing fuzzy production rules of knowledge base, the logic programming language Prolog was used. The created application is planned to be integrated into medical information systems. © 2022 CURRAN-CONFERENCE. All rights reserved.

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

Using a Decision Tree with a Feedback Function to Select Therapeutic Tactis for Viral Infection of the Respiratory Tract in the Medical Expert System

The Electronic Clinical Pharmacologist (ECP) is a Medical Decision Support System (MDSS). This system is based on the Unified Medical Knowledge Base (UMKB), which is updated and updated as new medicines are released and specialized publications are published in peer-reviewed biomedical scientific journals. ECP helps to reduce the risks of medical errors and complications in clinical practice. When using ECP, the number of side effects from the use of medicines decreases, the patient's admission time is reduced, the quality of medical care is improved, the costs of the medical organization for the purchase of medicines are reduced, all this is carried out due to more rational prescriptions of the doctor. The ECP takes into account the personalized approach of drug therapy. Based on Stavropol State Medical University and medical universities of the North Caucasus Federal District, a questionnaire of students was conducted among students of 3-6 courses, as well as testing of the ECP application to compare treatment standards (clinical recommendations) according to the clinical recommendations of the Ministry of Health (outpatient, inpatient treatment) and self-treatment of students and their relatives. And patterns of changes in the course of treatment were also revealed when using MDSS ECP and without it. (C) 2022 INT TRANS J ENG MANAG SCI TECH.

Artificial intelligence in identifying COVID-19 patients based on inflammatory parameters

The virus SARS-Co V -2 that has caused a pandemic of COVID-19 in 2019 is still a major concern for health care systems. The reason for this is the fact that the outcome of the disease is difficult to predict, as deadly complications can occur in all people. Diagnosing COVID-19 relies on polymerase chain reaction (PCR) testing and antigen testing, both of which require special referral. The aim of this study was to develop artificial intelligence (AI) expert system which will facilitate COVID-19 diagnosis based on parameters that can be readily collected from blood specimens. The database contains 1000 samples, divided into 2 categories: (1) healthy and (2) sick subjects The following parameters were used: CRP, LDH, SE, AST, ALT, D-dimer and IL-6. The sensitivity of the developed system was 100%, specificity 98.33%, and accuracy 99.67%, on the basis of which we can conclude that the use of AI in the diagnosis of COVID19 has a significant potential. © 2022 IEEE.

Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with COVID-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with these issues, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat in ICU. In this study, an interval type-2 fuzzy expert system is proposed for the prediction of ICU admission in COVID-19 patients. For this prediction task, we also developed an adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these fuzzy systems are compared to some well-known classification methods such as Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other diagnosis systems.

Decision support system to evaluate a vandalized and deteriorated oil pipeline transportation system using artificial intelligence techniques. Part 1: modeling

The oil and gas industry worldwide is experiencing problems of vandalism and mechanical deterioration due to corrosion in its various pipeline transport systems, a drop in the price of hydrocarbons due to the COVID-19, limitation of maintenance processes. This article provides a contribution original to the knowledge and management of a pipeline transportation system (PTS), without an immediate high impact that would help reduce property loss due to corrosion, through the development of intelligent evaluation models that combine field data, laboratory, and cognitive knowledge in a case study in Mexico. The research is divided into Part 1: modeling, a Fuzzy expert system (FES) unified the knowledge of corrosion specialists and mechanical integrity studies (MIS) and identified evolutionary corrosion patterns with reliability of 0.9029. An artificial neural network (ANN) supported by statistics and metallography establishes test reliability of 0.9556 and determines the corrosion inhibition capacity (C) of Mexican hydrocarbon mixtures based on their properties compared to carbon steel. Part 2: analysis of the operational and economic risk of the PTS under corrosive effects, using Monte Carlo simulation (MCS) estimates various financial scenarios considering corrosive profiles of soils, supply, demand, and inflation.