Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques.

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption.

Cancer-Related Malnutrition and the Role of Parenteral Nutrition in Cancer; A Narrative Review.

The growing incidence rate of cancer and its associated morbidity and mortality prompts the need to identify factors that could improve the quality of life (QoL) and survival of a patient with cancer. Cancer-associated malnutrition is a common complication that could start at the early stages of cancer and could further develop into advanced cachexia. Response to treatment, length of hospital stay, progression of infection, and other complications of cancer including chemotherapy adverse events could all be influenced by the progression of malnutrition. Nutritional interventions may vary from oral to enteral and parenteral therapy. Parenteral nutrition (PN) therapy may benefit patients at certain stages of cancer in whom contraindications or inefficacy of other modalities of nutritional support are present. This method may seem invasive, costly, and risky but at the same time may improve certain patients' QoL and chance of survival. In trained settings with proper facilities, this method of nutritional support can benefit patients; However, the indication for starting PN must be carefully supervised considering that other nutritional support methods may be equally efficient and at the same time easier to access and apply.

Design, synthesis and application of a magnetic H-bond catalyst in the preparation of new nicotinonitriles via cooperative vinylogous anomeric-based oxidation.

Herein, we designed and synthesized a new H-bond magnetic catalyst with 2-tosyl-N-(3-(triethoxysilyl)propyl)hydrazine-1-carboxamide as a sensitive H-bond donor/acceptor. We created an organic structure with a urea moiety on the magnetic nanoparticles, which can function as a hydrogen bond catalyst. Hydrogen bond catalysts serve as multi-donor/-acceptor sites. Additionally, we utilized magnetic nanoparticles in the production of the target catalyst, giving it the ability to be recycled and easily separated from the reaction medium with an external magnet. We evaluated the catalytic application of Fe3O4@SiO2@tosyl-carboxamide as a new magnetic H-bond catalyst in the synthesis of new nicotinonitrile compounds through a multicomponent reaction under solvent-free and green conditions with high yields (50-73%). We confirmed the structure of Fe3O4@SiO2@tosyl-carboxamide using various techniques. In addition, the structures of the desired nicotinonitriles were confirmed using melting point, 1H-NMR, 13C-NMR and HR-mass spectrometry analysis. The final step of the reaction mechanism was preceded via cooperative vinylogous anomeric-based oxidation (CVABO).

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review.

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

Developing public health surveillance dashboards: a scoping review on the design principles.

BACKGROUND: Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. METHODOLOGY: This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. RESULTS: Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. CONCLUSION: Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features.

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.

The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews.

Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.

Evaluation of cell adhesion molecules (LFA-1 and L-selectin) in ankylosing spondylitis patients after treatment with ß-D-mannuronic acid (M2000).

Background & objectives: To examine ß-D-mannuronic acid (M2000) effects on L-selectin shedding and leucocyte function-associated antigen-1 (LFA-1) expression as mechanisms of action of this drug in patients with ankylosing spondylitis (AS). Methods: To investigate the molecular consequences of ß-D-mannuronic acid on L-selectin shedding, flow cytometry method was used. Furthermore, the effect of it on LFA-1 gene expression was analyzed by using quantitative real time (qRT)-PCR technique. Results: The LFA-1 expression in patients with AS was higher than controls (P=0.046). The LFA-1 expression after 12 wk therapy with ß-D-mannuronic acid was meaningfully decreased (P=0.01). After 12 wk treatment with ß-D-mannuronic acid, the frequency of CD62L-expressing CD4+ T cells in patients with AS, was not considerably altered, compared to the patients before therapy (P=0.5). Furthermore, after 12 wk therapy with ß-D-mannuronic acid, L-selectin expression levels on CD4+ T-cells in patients with AS, were not remarkably changed, compared to the expression levels of these in patients before treatment (P=0.2). Interpretation & conclusions: The results of this study for the first time showed that ß-D-mannuronic acid can affect events of adhesion cascade in patients with AS. Moreover, ß-D-mannuronic acid presented as an acceptable benefit to AS patients and could aid in the process of disease management.

Photobiomodulation Therapy Improves Inflammatory Responses by Modifying Stereological Parameters, microRNA-21 and FGF2 Expression.

Introduction: Photobiomodulation treatment (PBMT) is a relatively invasive method for treating wounds. An appropriate type of PBMT can produce desired and directed cellular and molecular processes. The aim of this study was to investigate the impacts of PBMT on stereological factors, bacterial count, and the expression of microRNA-21 and FGF2 in an infected, ischemic, and delayed wound healing model in rats with type one diabetes mellitus. Methods: A delayed, ischemic, and infected wound was produced on the back skin of all 24 DM1 rats. Then, they were put into 4 groups at random (n=6 per group): 1=Control group day4 (CGday4); 2=Control group day 8 (CGday8); 3=PBMT group day4 (PGday4), in which the rats were exposed to PBMT and killed on day 4; 4=PBMT group day8 (PGday8), in which the rats received PBMT and they were killed on day 8. The size of the wound, the number of microbial colonies, stereological parameters, and the expression of microRNA-21 and FGF2 were all assessed in this study throughout the inflammation (day 4) and proliferation (day 8) stages of wound healing. Results: On days 4 and 8, we discovered that the PGday4 and PGday8 groups significantly improved stereological parameters in comparison with the same CG groups. In terms of ulcer area size and microbiological counts, the PGday4 and PGday8 groups performed much better than the same CG groups. Simultaneously, the biomechanical findings in the PGday4 and PGday8 groups were much more extensive than those in the same CG groups. On days 4 and 8, the expression of FGF2 and microRNA-21 was more in all PG groups than in the CG groups (P<0.01). Conclusion: PBMT significantly speeds up the repair of ischemic and MARS-infected wounds in DM1 rats by lowering microbial counts and modifying stereological parameters, microRNA-21, and FGF2 expression.

Application of Nanomagnetic Metal-Organic Frameworks in the Green Synthesis of Nicotinonitriles via Cooperative Vinylogous Anomeric-Based Oxidation.

In the current study, we synthesized a new nanomagnetic metal-organic framework Fe3O4@MIL-53(Al)-N(CH2PO3)2 and characterized it using various techniques. This nanomagnetic metal-organic framework was used for the synthesis of a wide range of nicotinonitrile derivatives as suitable drug candidates by a four-component reaction of 3-oxo-3-phenylpropanenitrile or 3-(4-chlorophenyl)-3-oxopropanenitrile, ammonium acetate (NH4OAc), acetophenone derivatives, and various aldehydes including those bearing electron-donating, electron-withdrawing, and halogen groups, which afforded desired products (27 samples) via a cooperative vinylogous anomeric-based oxidation (CVABO) mechanism under solvent-free conditions in excellent yields (68-90%) and short reaction times (40-60 min). Increasing the surface-to-volume ratio, easy separation of the catalyst using an external magnet, and high chemical and temperature stability are the advantages of the described nanomagnetic metal-organic frameworks.

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees.

The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).

Plasmon-enhanced nano-photosensitizers: game-changers in photodynamic therapy of cancers.

Plasmonic nanostructures can be used to tackle the shortcomings of conventional photosensitizers in photodynamic therapy (PDT) of cancers, including their low reactive oxygen species (ROS) quantum yield, stability, and targetability. However, the positive role of plasmonic nanostructures is not limited to their ability for ROS generation or singlet oxygen formation. The main advantage of plasmonic nanostructures relies on the collective oscillation of free electrons, the so-called surface plasmon resonance (SPR), which can trigger plenty of optical phenomena in their near-field. Surface plasmon resonance is highly dependent on the morphology, size, and composition of the plasmonic nanostructure, which can give one the ability to control the wavelength of light-matter interaction, which is highly desirable in PDT applications. This review has focused on the conjugation of plasmonic nanostructures with organic compounds, biological compounds, ceramic nanoparticles, polymeric nanoparticles, metal-organic frameworks (MOFs), and magnetic nanoparticles from a mechanistic point of view. Hybridization of plasmonic nanoparticles would enable plenty of optical mechanisms beneficial for the PDT process that has been extensively discussed by presenting the most recent efforts in each category. This review can be a useful guideline for researchers working on enhancing the efficiency of the PDT process and those interested in plasmon-enhanced phenomena by emphasizing the underlying mechanisms.

Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review.

The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.

Usability Evaluation of Dashboards: A Systematic Literature Review of Tools.

Introduction: In recent years, the use of dashboards in healthcare has been considered an effective approach for the visual presentation of information to support clinical and administrative decisions. Effective and efficient use of dashboards in clinical and managerial processes requires a framework for the design and development of tools based on usability principles. Objectives: The present study is aimed at investigating the existing questionnaires used for the usability evaluation framework of dashboards and at presenting more specific usability criteria for evaluating dashboards. Methods: This systematic review was conducted using PubMed, Web of Science, and Scopus, without any time restrictions. The final search of articles was performed on September 2, 2022. Data collection was performed using a data extraction form, and the content of selected studies was analyzed based on the dashboard usability criteria. Results: After reviewing the full text of relevant articles, a total of 29 studies were selected according to the inclusion criteria. Regarding the questionnaires used in the selected studies, researcher-made questionnaires were used in five studies, while 25 studies applied previously used questionnaires. The most widely used questionnaires were the System Usability Scale (SUS), Technology Acceptance Model (TAM), Situation Awareness Rating Technique (SART), Questionnaire for User Interaction Satisfaction (QUIS), Unified Theory of Acceptance and Use of Technology (UTAUT), and Health Information Technology Usability Evaluation Scale (Health-ITUES), respectively. Finally, dashboard evaluation criteria, including usefulness, operability, learnability, ease of use, suitability for tasks, improvement of situational awareness, satisfaction, user interface, content, and system capabilities, were suggested. Conclusion: General questionnaires that were not specifically designed for dashboard evaluation were mainly used in reviewed studies. The current study suggested specific criteria for measuring the usability of dashboards. When selecting the usability evaluation criteria for dashboards, it is important to pay attention to the evaluation objectives, dashboard features and capabilities, and context of use.

Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review.

Method: This study was conducted according to Arksey and O'Malley's framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020. Results: Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients. Conclusion: The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Virtual reality games for rehabilitation of upper extremities in stroke patients.

INTRODUCTION: Stroke is one of the main causes of physical disability in which doing frequent and early exercise is imperative for rehabilitation. Virtual reality gaming has a high potential in rehabilitation leading to increased performance of patients. This study aimed to develop, validate and examine virtual reality games in chronic stroke patients. METHODS: This was a single before-after study. To determine the movements and content of games, 9 physiotherapists and 11 game designers were asked to participate in a questionnaire-based survey. Then, to evaluate the impact of games on rehabilitation, patients (N = 10; mean age = 52 ± 4.38) with chronic stroke were asked to play the games three times a week for four weeks. Outcomes included measurement of the ability to perform shoulder, elbow and wrist movements was performed using goniometric instrument, Modified Motor Assessment Scale (MMAS) was used to assess the functional ability of patients and muscle spasticity, and brunnstrom's stages of recovery test was also used to assess spastic and involuntary muscle movement. RESULTS: Games have positive effects on the horizontal abduction of shoulder (16.26 ± 23.94, P = 0.02), horizontal adduction of shoulder (59.24 ± 74.76, P = 0.00), supination of wrist (10.68 ± 53.52, P = 0.02), elbow flexion (0.1 ± 1.5, P = 0.00), and wrist flexion (0.06 ± 1.34, P = 0.03). However, they had no effects on the flexion of shoulder, flexion of elbow, extension of elbow, and extension of wrist (p-value> 0.05). CONCLUSIONS: The results showed that games improve the range of motion of the participants in terms of horizontal abduction and abduction of the shoulder, elbow flexion, and supination and flexion of the wrist. Due to the small sample size in this study, we recommend more studies with larger samples and a control group.

A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique.

BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification.

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.

Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review.

BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices. METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis. RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected. CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.