Introduction: Diabetes disease is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced. Designing an automated system for regulating blood glucose in patients with diabetes is a solution that researchers have been paying close attention to in recent years. Therefore, safety is the minimum requirement for safety-critical systems such as the artificial pancreas. The present study introduces a safe, robust, performance-guaranteed optimal controller that can safely regulate blood glucose in the disturbance.Material and Methods: In this section, first, regulate blood glucose levels in simulation studies is evaluated. For this purpose, a dynamic model is used. The model includes a virtual patient, an insulin pump, and a continuous blood glucose level sensor. The virtual patient model represents the dynamics of insulin-glucose, carbohydrate-glucose, and exercise-glucose.Results: The need to not reset the controller parameters for patients in each category is one of the suggested controller's benefits. However, the PID controller needs to reset the parameters for each group of patients, the predictive control method requires the estimated model of the patient, and its performance is different on different days because the insulin-glucose dynamics for an individual changes day by day.Conclusion: Taking into account different sensitivities of body tissue to insulin, the results of evaluating the controller for two different groups of patients have shown that the controller is resistant to day-to-day changes in patients who may experience changes in insulin sensitivity, even with stress or medication and will not lose its optimal function. Based on the simulation results, the proposed controller can reduce the external disturbances' effect, whose amplitude is to a good extent within the body's physiological range.
{"title":"Blood Glucose Regulation in Patients with Type 1 Diabetes by Robust Optimal Safety Critical Control","authors":"Navid Moshtaghi Yazdani, R. Kardehi Moghaddam","doi":"10.30699/fhi.v10i1.286","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.286","url":null,"abstract":"Introduction: Diabetes disease is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced. Designing an automated system for regulating blood glucose in patients with diabetes is a solution that researchers have been paying close attention to in recent years. Therefore, safety is the minimum requirement for safety-critical systems such as the artificial pancreas. The present study introduces a safe, robust, performance-guaranteed optimal controller that can safely regulate blood glucose in the disturbance.Material and Methods: In this section, first, regulate blood glucose levels in simulation studies is evaluated. For this purpose, a dynamic model is used. The model includes a virtual patient, an insulin pump, and a continuous blood glucose level sensor. The virtual patient model represents the dynamics of insulin-glucose, carbohydrate-glucose, and exercise-glucose.Results: The need to not reset the controller parameters for patients in each category is one of the suggested controller's benefits. However, the PID controller needs to reset the parameters for each group of patients, the predictive control method requires the estimated model of the patient, and its performance is different on different days because the insulin-glucose dynamics for an individual changes day by day.Conclusion: Taking into account different sensitivities of body tissue to insulin, the results of evaluating the controller for two different groups of patients have shown that the controller is resistant to day-to-day changes in patients who may experience changes in insulin sensitivity, even with stress or medication and will not lose its optimal function. Based on the simulation results, the proposed controller can reduce the external disturbances' effect, whose amplitude is to a good extent within the body's physiological range.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hassan Vakili Arki, Ehsan Nabovati, M. Saberi, Pourya Eslami, Z. Taherzadeh, S. Eslami
Introduction: Irrational prescription of antibiotics has become a major global concern, and not only does it have health-related consequences, but it also affects countries’ overall economy. Based on reports and studies, antibiotics are prescribed in approximately 50% of prescriptions in Iran which can demand by patients as a major cause. It is anticipated that increasing the awareness and understanding of both physicians and patients, regarding the antibiotic use and resistance, could play an important role in the rational prescription of antibiotic medications. In this study, we will examine the effect of informing patients via text message right before their appointment on the proportion of prescribed antibiotic medications.Material and Methods: In this study, a randomized control trial (RCT) will be conducted. The setting in which the study will be carry out, consists of 64 physicians (29 general physician and 35 specialist). Unit of randomization will be physicians based on the proportion of their prescriptions that include antibiotic medications (PIA). The first arm of the study is the intervention group, which consists of the patients receiving three text messages in the clinic’s waiting rooms. The second arm is the control group, and consists of the patients who won’t be receiving any text messages. The content of the text messages focuses on the consequences of self-medication with antibiotics, the fact that the use of antibiotics is not an option for curing viral diseases including cold, and it also asks the patients not to demand antibiotics by trusting their physicians.Results: The main variable that will be measured is the proportion of prescriptions that include antibiotic medications.Conclusion: This trial will be the first one to evaluate the patients’ role in the proportion of prescriptions that include antibiotic medications. It is hypothesized that patients’ demand for antibiotic medication is one of the main causes of irrational antibiotic prescription by physicians.
{"title":"Evaluation of the effect of informing patients through text messaging on antibiotic prescription by physicians in outpatient setting: a study protocol","authors":"Hassan Vakili Arki, Ehsan Nabovati, M. Saberi, Pourya Eslami, Z. Taherzadeh, S. Eslami","doi":"10.30699/fhi.v10i1.281","DOIUrl":"https://doi.org/10.30699/fhi.v10i1.281","url":null,"abstract":"Introduction: Irrational prescription of antibiotics has become a major global concern, and not only does it have health-related consequences, but it also affects countries’ overall economy. Based on reports and studies, antibiotics are prescribed in approximately 50% of prescriptions in Iran which can demand by patients as a major cause. It is anticipated that increasing the awareness and understanding of both physicians and patients, regarding the antibiotic use and resistance, could play an important role in the rational prescription of antibiotic medications. In this study, we will examine the effect of informing patients via text message right before their appointment on the proportion of prescribed antibiotic medications.Material and Methods: In this study, a randomized control trial (RCT) will be conducted. The setting in which the study will be carry out, consists of 64 physicians (29 general physician and 35 specialist). Unit of randomization will be physicians based on the proportion of their prescriptions that include antibiotic medications (PIA). The first arm of the study is the intervention group, which consists of the patients receiving three text messages in the clinic’s waiting rooms. The second arm is the control group, and consists of the patients who won’t be receiving any text messages. The content of the text messages focuses on the consequences of self-medication with antibiotics, the fact that the use of antibiotics is not an option for curing viral diseases including cold, and it also asks the patients not to demand antibiotics by trusting their physicians.Results: The main variable that will be measured is the proportion of prescriptions that include antibiotic medications.Conclusion: This trial will be the first one to evaluate the patients’ role in the proportion of prescriptions that include antibiotic medications. It is hypothesized that patients’ demand for antibiotic medication is one of the main causes of irrational antibiotic prescription by physicians.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130599344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-03DOI: 10.21203/RS.3.RS-294115/V1
Peter T. Habib
Introduction: The infections with the Nipah virus (NiV) are highly infectious and may lead to severe febrile encephalitis. High mortality rates in southeastern Asia, including Bengal, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, the Philippines, Thailand, and India, have been reported in NiV outbreaks. Considering the high risk of an epidemic, NiV was declared a priority pathogen by the World Health Organization. However, for the treatment of this infection, there is no effective therapy or approved FDA medicines. RNA-dependent polymerase RNA (RdRp) plays an important role in viral replication among the nine well-known proteins of NiV.Material and Methods: Fourteen antiviral molecules have been computerized for NiV RNA-dependent RNA polymerase and demonstrated a potential inhibition effect against coronavirus (NiV-RdRp). A multi-step molecular docking process, followed by extensive analyzes of molecular binding interactions, binding energy estimates, synthetic accessibility assessments, and toxicity tests.Results: Molecular docking analysis reveals that Uprifosbuvir is the most suitable inhibitor for RdRp of Nipah Virus regarding the binding affinity and binding in the target cavity. Although, such studies need clinical confirmation.Conclusion: The role of anti-viral molecules as a ligand against RNA-dependent RNA polymerase is critical important in the current era. Computational tools such as molecular docking has proven its power in the analysis of molecules interaction. Our analysis reveals the Uprifosbuvir might be a candidate RdRp inhibitor. This study should further investigate the properties of the already identified anti-viral molecules followed by a pharmacological investigation of these in-silico findings in suitable models.
{"title":"Investigation of Anti-Coronavirus, Anti-HCV, Nucleotide Inhibitors, and Bioactive Molecules efficacy Against RNA-directed RNA polymerase of Nipah Virus: Molecular Docking Study","authors":"Peter T. Habib","doi":"10.21203/RS.3.RS-294115/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-294115/V1","url":null,"abstract":"Introduction: The infections with the Nipah virus (NiV) are highly infectious and may lead to severe febrile encephalitis. High mortality rates in southeastern Asia, including Bengal, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, the Philippines, Thailand, and India, have been reported in NiV outbreaks. Considering the high risk of an epidemic, NiV was declared a priority pathogen by the World Health Organization. However, for the treatment of this infection, there is no effective therapy or approved FDA medicines. RNA-dependent polymerase RNA (RdRp) plays an important role in viral replication among the nine well-known proteins of NiV.Material and Methods: Fourteen antiviral molecules have been computerized for NiV RNA-dependent RNA polymerase and demonstrated a potential inhibition effect against coronavirus (NiV-RdRp). A multi-step molecular docking process, followed by extensive analyzes of molecular binding interactions, binding energy estimates, synthetic accessibility assessments, and toxicity tests.Results: Molecular docking analysis reveals that Uprifosbuvir is the most suitable inhibitor for RdRp of Nipah Virus regarding the binding affinity and binding in the target cavity. Although, such studies need clinical confirmation.Conclusion: The role of anti-viral molecules as a ligand against RNA-dependent RNA polymerase is critical important in the current era. Computational tools such as molecular docking has proven its power in the analysis of molecules interaction. Our analysis reveals the Uprifosbuvir might be a candidate RdRp inhibitor. This study should further investigate the properties of the already identified anti-viral molecules followed by a pharmacological investigation of these in-silico findings in suitable models.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126442285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: The SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global epidemic that has increased the scientific community's concern about developing and finding a counteraction against this lethal virus. So far, hundreds of thousands of people have been infected by the pandemic due to contamination and spread. This research was therefore carried out to develop potential epitope-based vaccines against the SARS-CoV-2 virus using reverse vaccinology and immunoinformatics approaches.Material and Methods: The material of SARS-COV2 Surface Glycoprotein (S), Membrane Protein (M), and Envelope Protein (E) were downloaded from the NCBI protein database. Each protein has undergone epitopes prediction for MHC class I epitopes, MHC class II epitopes, and Antibody of B-cell epitopes. Selected epitopes according to their antigenicity score was tested for allergenicity and toxicity. Finally, filtered epitopes were used in vaccine construction. Vaccines were constructed, docked against Toll-like receptor 3, and undergone Molecular Dynamic simulation. The vaccine with the best scores, subjected to immune stimulation and cloning design.Results: Three vaccines were constructed, COVac-1, COVac-2, and COVac-3. Each vaccine was submitted into a deep investigation. The molecular dynamic simulation determines the stability and physical movement of protein atoms and molecules. After Molecular dynamics simulation, COVac-1 was having the best scores. COVac-1 was then subjected to immune simulation analysis to insure the stimulation of innate and adaptive immunity. After passing the immune simulation, COVac-1 was integrated into E.coli pET-30b plasmid using in silico cloning design.Conclusion:Viral pandemics are threatened to face humanity today. The best scenario to fight against any pandemic is utilizing the full power of computational biology, especially immune-informatics, to design and discover in silico new vaccines or molecules that may stimulate the immune system against the invader pathogens or inhibit the pathogen life cycle.
{"title":"Vaccine Design, Adaptation, and Cloning Design for Multiple Epitope-Based Vaccine Derived From SARS-CoV-2 Surface Glycoprotein (S), Membrane Protein (M) and Envelope Protein (E): In silico approach","authors":"Peter T. Habib","doi":"10.30699/FHI.V10I1.279","DOIUrl":"https://doi.org/10.30699/FHI.V10I1.279","url":null,"abstract":"Introduction: The SARS Coronavirus-2 (SARS-CoV-2) pandemic has become a global epidemic that has increased the scientific community's concern about developing and finding a counteraction against this lethal virus. So far, hundreds of thousands of people have been infected by the pandemic due to contamination and spread. This research was therefore carried out to develop potential epitope-based vaccines against the SARS-CoV-2 virus using reverse vaccinology and immunoinformatics approaches.Material and Methods: The material of SARS-COV2 Surface Glycoprotein (S), Membrane Protein (M), and Envelope Protein (E) were downloaded from the NCBI protein database. Each protein has undergone epitopes prediction for MHC class I epitopes, MHC class II epitopes, and Antibody of B-cell epitopes. Selected epitopes according to their antigenicity score was tested for allergenicity and toxicity. Finally, filtered epitopes were used in vaccine construction. Vaccines were constructed, docked against Toll-like receptor 3, and undergone Molecular Dynamic simulation. The vaccine with the best scores, subjected to immune stimulation and cloning design.Results: Three vaccines were constructed, COVac-1, COVac-2, and COVac-3. Each vaccine was submitted into a deep investigation. The molecular dynamic simulation determines the stability and physical movement of protein atoms and molecules. After Molecular dynamics simulation, COVac-1 was having the best scores. COVac-1 was then subjected to immune simulation analysis to insure the stimulation of innate and adaptive immunity. After passing the immune simulation, COVac-1 was integrated into E.coli pET-30b plasmid using in silico cloning design.Conclusion:Viral pandemics are threatened to face humanity today. The best scenario to fight against any pandemic is utilizing the full power of computational biology, especially immune-informatics, to design and discover in silico new vaccines or molecules that may stimulate the immune system against the invader pathogens or inhibit the pathogen life cycle.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124012748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction- Colorectal cancer (CRC) is a development of abnormal cells either in colon or rectum. CRC considered being the 3rd leading cause of death in 2018 only behind lung and breast cancer. It first arises during pre-cancerous stages called as polyps. The detection and removal of polyp is important to increase the survival rate of patient. Various method of polyp detection are available. However, only colonoscopy remains the gold standard in detection and removal of polyps. Several studies showed how Artificial Intelligence (AI) used in colonoscopy area particularly in detecting polyps, assessing physicians and predicting patient with high risk of CRC. The aim of this study is to describe the involvement of AI in colonoscopy and its impact in reducing the Materials and methods– Search for journal articles conducted between May and June 2016 from various resources including PubMed and Google Scholar. 6 research journals were reviewed and all the advantages and limitations were discussed throughout this study. Results– Various study showed that AI able to improve medical diagnostic of CRC in several ways, including in the improvement of adenoma detection rate (ADR) in terms of medical diagnostic, finding physicians associated with high Adenoma Detection Rate (ADR) and predicting patients with high risk of CRC. In addition, the use of AI in colonoscopy also associated with limitations including require large amount of datasets and advance computational resources in order to generate accurate output. Conclusion– The utilization of AI in colonoscopy shows how it able to improve the diagnosis accuracy and survival rate of patients associated with CRC despite several limitations that were identified during the study. However in the future, instead of allowing it to fully automatically conducting diagnosis, it still needs to be accompanied by physicians conducting the operation as there is no hundred percent perfect algorithms.
{"title":"Artificial Intelligence in Colonoscopy: Improving Medical Diagnostic of Colorectal Cancer","authors":"S. Bernard, A. A. Parikesit","doi":"10.30699/FHI.V9I1.209","DOIUrl":"https://doi.org/10.30699/FHI.V9I1.209","url":null,"abstract":"Introduction- Colorectal cancer (CRC) is a development of abnormal cells either in colon or rectum. CRC considered being the 3rd leading cause of death in 2018 only behind lung and breast cancer. It first arises during pre-cancerous stages called as polyps. The detection and removal of polyp is important to increase the survival rate of patient. Various method of polyp detection are available. However, only colonoscopy remains the gold standard in detection and removal of polyps. Several studies showed how Artificial Intelligence (AI) used in colonoscopy area particularly in detecting polyps, assessing physicians and predicting patient with high risk of CRC. The aim of this study is to describe the involvement of AI in colonoscopy and its impact in reducing the Materials and methods– Search for journal articles conducted between May and June 2016 from various resources including PubMed and Google Scholar. 6 research journals were reviewed and all the advantages and limitations were discussed throughout this study. Results– Various study showed that AI able to improve medical diagnostic of CRC in several ways, including in the improvement of adenoma detection rate (ADR) in terms of medical diagnostic, finding physicians associated with high Adenoma Detection Rate (ADR) and predicting patients with high risk of CRC. In addition, the use of AI in colonoscopy also associated with limitations including require large amount of datasets and advance computational resources in order to generate accurate output. Conclusion– The utilization of AI in colonoscopy shows how it able to improve the diagnosis accuracy and survival rate of patients associated with CRC despite several limitations that were identified during the study. However in the future, instead of allowing it to fully automatically conducting diagnosis, it still needs to be accompanied by physicians conducting the operation as there is no hundred percent perfect algorithms. ","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Artificial intelligence (AI) research within medicine is growing rapidly. AI is poised to transform medical practice. AI has been studied in several areas of healthcare and medical practice, including diagnosing, treating and caring of patients. Warfarin is one of the most commonly prescribed oral anticoagulant. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. The purpose of this study was to determine exact dose of warfarin needed for patients with artificial heart valve using artificial neural networks (ANN).Development: To achieved the best model, some multi-layer perceptron ANNs were constructed with different structures. The dataset used included 846 patients who had been referred to the PT clinic in Tehran heart center in the second six months of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated and used for prediction system developments. In this paper the implementation of ANNs and proposed system in MatLab environment are described.Application: The effectiveness of ANNs were evaluated in terms of classification performance using 10fold cross-validation procedure and the results showed that the best model is a network that has 7 neurons in its hidden layer with an average absolute error of 0.1, disturbance rate of 0.33 and regression of 0.87. Conclusion: The achieved results reveal that ANN-based system is a suitable tool for warfarin dose prediction in Iranian patients with an artificial heartvalve. However, no system can be guaranteed to achieve 100% accuracy, but using such methods can reduce medical errors and thereby improve health care and patient safety.
{"title":"Developing an Intelligent System for Prediction of Optimal Dose of Warfarin in Iranian Adult Patients with Artificial Heart Valve","authors":"M. Aghazadeh, A. Orooji, Mehran Kamkar Haghighi","doi":"10.30699/fhi.v8i1.213","DOIUrl":"https://doi.org/10.30699/fhi.v8i1.213","url":null,"abstract":"Introduction: Artificial intelligence (AI) research within medicine is growing rapidly. AI is poised to transform medical practice. AI has been studied in several areas of healthcare and medical practice, including diagnosing, treating and caring of patients. Warfarin is one of the most commonly prescribed oral anticoagulant. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. The purpose of this study was to determine exact dose of warfarin needed for patients with artificial heart valve using artificial neural networks (ANN).Development: To achieved the best model, some multi-layer perceptron ANNs were constructed with different structures. The dataset used included 846 patients who had been referred to the PT clinic in Tehran heart center in the second six months of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated and used for prediction system developments. In this paper the implementation of ANNs and proposed system in MatLab environment are described.Application: The effectiveness of ANNs were evaluated in terms of classification performance using 10fold cross-validation procedure and the results showed that the best model is a network that has 7 neurons in its hidden layer with an average absolute error of 0.1, disturbance rate of 0.33 and regression of 0.87. Conclusion: The achieved results reveal that ANN-based system is a suitable tool for warfarin dose prediction in Iranian patients with an artificial heartvalve. However, no system can be guaranteed to achieve 100% accuracy, but using such methods can reduce medical errors and thereby improve health care and patient safety.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132953594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Nowadays, medical sciences and physicians face a huge amount of data. Diabetes is one of the most expensive glands in the world. Since it is not always easy to diagnose the disease, the physician should examine the outcome of patient tests and decisions made in the past for patients with similar conditions to make an appropriate decision. Due to the large number of patients and the multiple tests performed on each patient, an automated tool for exploring previous patients is needed.Materials and Methods: One of the most important methods used to derive data is data mining. Due to the high number of diabetic patients, timely diagnosis and treatment of this disease can reduce the risk of death and its associated medical costs. So far, different systems have been proposed for the diagnosis and prediction of diabetes, but fuzzy logic based systems are used in this study to increase accuracy and efficiency. In the proposed model, fuzzy clustering is first grouped into separate clusters, and then the radial neural network is predicted for each patient with diabetes mellitus. A compatible neuro-fuzzy inference system has also been used to diagnose diabetes.Results: In this paper different classification techniques have been used in MATLAB software to diagnose diabetes mellitus and to classify patients as diabetic and non diabetic. The dataset used is extracted from the UCI database. The accuracy of the proposed method is 97.14% which is significantly higher than other models of diabetes diagnosis.Conclusion: The application of two fuzzy models has significantly improved the accuracy of diagnosis of diabetes compared to other models proposed in this field.
{"title":"Improving Diagnosis Accuracy of Diabetic Disease Using Radial Basis Function Network and Fuzzy Clustering","authors":"H. Hosseini, Amid Khatibi Bardsiri","doi":"10.30699/fhi.v8i1.203","DOIUrl":"https://doi.org/10.30699/fhi.v8i1.203","url":null,"abstract":"Introduction: Nowadays, medical sciences and physicians face a huge amount of data. Diabetes is one of the most expensive glands in the world. Since it is not always easy to diagnose the disease, the physician should examine the outcome of patient tests and decisions made in the past for patients with similar conditions to make an appropriate decision. Due to the large number of patients and the multiple tests performed on each patient, an automated tool for exploring previous patients is needed.Materials and Methods: One of the most important methods used to derive data is data mining. Due to the high number of diabetic patients, timely diagnosis and treatment of this disease can reduce the risk of death and its associated medical costs. So far, different systems have been proposed for the diagnosis and prediction of diabetes, but fuzzy logic based systems are used in this study to increase accuracy and efficiency. In the proposed model, fuzzy clustering is first grouped into separate clusters, and then the radial neural network is predicted for each patient with diabetes mellitus. A compatible neuro-fuzzy inference system has also been used to diagnose diabetes.Results: In this paper different classification techniques have been used in MATLAB software to diagnose diabetes mellitus and to classify patients as diabetic and non diabetic. The dataset used is extracted from the UCI database. The accuracy of the proposed method is 97.14% which is significantly higher than other models of diabetes diagnosis.Conclusion: The application of two fuzzy models has significantly improved the accuracy of diagnosis of diabetes compared to other models proposed in this field.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130004129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nasim Saboorizadeh, M. Firoozabadi, N. Mohammadzadeh
Introduction: Many diseases require constant monitoring todays, and online communication with patients for timely intervention is necessary. In this study, based on the results of these studies, we investigated the factors affecting telemedicine admission in Parkinson's patients.Material and methods: This research was a descriptive survey. The tool of this research is a researcher-made questionnaire that was based on library and internet studies in valid databases such as Medline, Science direct (Elsevier), and searching for original research articles between 2000 and 2017. To search for keywords in the design of a telemedicine software, Parkinson's disease, Technology Acceptance Model in English-language databases.The questions were designed with the Likert spectrum. The validity of the questionnaire was assessed by the opinions of five experts. Content validity index was measured and item with CVI score higher than 0.79 was considered appropriate. Reliability was assessed through Cronbach's alpha. Statistical sample was determined using sample size determination method in two cities of Tehran and Shiraz. In this study, structural equation modeling (SEM) was used. SPSS software version 16 was used for data analysis. The final data analysis was done by modeling in Smart PLS version 3 softwareResults: For each t-statistic, the path between the two variables was examined, and the statistics whose magnitude was greater than 1.96, at a confidence level of 95%, considering the same path that represents the strength and power of the effect between the two variables, the research hypotheses were statistically and in sample. Examined. Of the 19 hypotheses considered for the adoption of the research technology model, 16 were accepted.Conclusion: Ease of use is one of the most influential factors on attitudes in Parkinson's patients in Iran. Technology anxiety is one of the most important factor that reduce the acceptance of portable smart systems. The Parkinson's patient user does not recognize recreation as a useful system, but the inclusion of educational content to promote health in the program will make Parkinson's patients more welcomed. If the software is prescribed by the therapist, its acceptance rate in Parkinson's patients will increase.
导言:现在很多疾病都需要持续监测，与患者在线沟通，及时干预是必要的。本研究在这些研究结果的基础上，探讨了影响帕金森病患者远程医疗入院的因素。材料与方法:本研究为描述性调查。本研究的工具是一份研究人员制作的问卷，该问卷基于Medline、Science direct(爱思唯尔)等有效数据库中的图书馆和互联网研究，并搜索2000年至2017年间的原创研究文章。在英语数据库中检索远程医疗软件设计中的关键词，帕金森病，技术接受模型。这些问题是按照李克特谱设计的。问卷的效度由五位专家的意见来评估。测量内容效度指数，认为CVI得分大于0.79的项目为适宜项目。通过Cronbach’s alpha评估信度。采用样本量测定法对德黑兰和设拉子两个城市进行统计样本测定。本研究采用结构方程模型(SEM)。采用SPSS 16版软件进行数据分析。最后的数据分析是在Smart PLS version 3软件中建模完成的。结果:对于每个t统计量，检查了两个变量之间的路径，并且在95%的置信水平下，考虑到代表两个变量之间影响强度和威力的相同路径，研究假设是统计和样本内的。检查。在考虑采用研究技术模型的19个假设中，有16个被接受。结论:易用性是影响伊朗帕金森病患者态度的重要因素之一。技术焦虑是降低便携式智能系统接受度的最重要因素之一。帕金森患者用户并不认为娱乐是一个有用的系统，但在程序中包含促进健康的教育内容将使帕金森患者更受欢迎。如果软件是由治疗师开的处方，帕金森患者的接受率将会提高。
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The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.
{"title":"Big Data from A to Z","authors":"Elham Nazari, Marziyeh Afkanpour, H. Tabesh","doi":"10.30699/FHI.V8I1.202","DOIUrl":"https://doi.org/10.30699/FHI.V8I1.202","url":null,"abstract":"The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130724005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Moghbeli, M. Langarizadeh, Yashil Ahadi Moghadam, Susan Hasanpour Heidari
Introduction: Worldwide, people living in rural and remote area with the lack of access to medical care are vulnerable. Despite of improvement in dentistry, dental caries is still one of the prevalent problems among people. By using tele-dentistry and transforming electronic patient information, dental services can be delivered specially in remote area so leads to improve public dental health.Methods: This is an applied development study and 24 dentists and dental radiologists were participated in this survey. The questionnaire was filled out by participant to obtain information about requirement for designing the conceptual model for teleconsultation system for dental problems. Collected data was analyzed by descriptive statics with SPSS version 22 software.Results: According to results patient name and last name, gender, emergency level, sign of the problem, patient history in patient demographic and clinical information section and in system capabilities section, dedicate the space for dentist to diagnosis and the space for seeing diagnosis report by patient was the 100 percent requirement considered. After requirement analysis conceptual model as use case diagram was designed.Conclusion: According to results, using tele-dentistry can improve relationship between specialists and patients without considering the distance and eventually improve public oral health in society.
{"title":"Design the Conceptual Model of Teleconsultation System for Dental Diseases","authors":"F. Moghbeli, M. Langarizadeh, Yashil Ahadi Moghadam, Susan Hasanpour Heidari","doi":"10.30699/fhi.v8i1.205","DOIUrl":"https://doi.org/10.30699/fhi.v8i1.205","url":null,"abstract":"Introduction: Worldwide, people living in rural and remote area with the lack of access to medical care are vulnerable. Despite of improvement in dentistry, dental caries is still one of the prevalent problems among people. By using tele-dentistry and transforming electronic patient information, dental services can be delivered specially in remote area so leads to improve public dental health.Methods: This is an applied development study and 24 dentists and dental radiologists were participated in this survey. The questionnaire was filled out by participant to obtain information about requirement for designing the conceptual model for teleconsultation system for dental problems. Collected data was analyzed by descriptive statics with SPSS version 22 software.Results: According to results patient name and last name, gender, emergency level, sign of the problem, patient history in patient demographic and clinical information section and in system capabilities section, dedicate the space for dentist to diagnosis and the space for seeing diagnosis report by patient was the 100 percent requirement considered. After requirement analysis conceptual model as use case diagram was designed.Conclusion: According to results, using tele-dentistry can improve relationship between specialists and patients without considering the distance and eventually improve public oral health in society.","PeriodicalId":154611,"journal":{"name":"Frontiers in Health Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125392724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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