Until May 16th, there are more than four million people who have been confirmed of the Covid-19 virus in the whole world and 311,739 of total deaths. The virus caused disastrous effects in the economy around the whole world, destroying small businesses and the stock market, ruining international transportation, devastating the morale of the people and prohibiting people from interacting and socializing. Considering these huge impacts that the virus made, on March, 12, 2020, the World Health Organization (WHO) characterized the Covid-19 caused by the Sars-CoV-2 virus as a global pandemic. There is not an effective vaccination or specific medicine to cure the disease. The most effective way to slow down the transmission is early detection, isolation of new carriers and operating proper treatment to patients. Thus, the research on the physical properties and clinical characteristics of the Covid-19 become significantly important. To prepare for the future prevention, this paper summarizes the overall treatment of the virus, mainly through the virus's origin, etiology, epidemiology, and clinical symptoms to inform readers more about the Covid-19, eliminate misunderstanding and bias to the virus, invoke the sense of self-protection and finally use scientific and logical methods to overcome this world-wide pandemic.
{"title":"Summarize the Etiology and Epidemiology Characteristics of the New Coronavirus","authors":"S. Shen","doi":"10.1145/3418094.3418142","DOIUrl":"https://doi.org/10.1145/3418094.3418142","url":null,"abstract":"Until May 16th, there are more than four million people who have been confirmed of the Covid-19 virus in the whole world and 311,739 of total deaths. The virus caused disastrous effects in the economy around the whole world, destroying small businesses and the stock market, ruining international transportation, devastating the morale of the people and prohibiting people from interacting and socializing. Considering these huge impacts that the virus made, on March, 12, 2020, the World Health Organization (WHO) characterized the Covid-19 caused by the Sars-CoV-2 virus as a global pandemic. There is not an effective vaccination or specific medicine to cure the disease. The most effective way to slow down the transmission is early detection, isolation of new carriers and operating proper treatment to patients. Thus, the research on the physical properties and clinical characteristics of the Covid-19 become significantly important. To prepare for the future prevention, this paper summarizes the overall treatment of the virus, mainly through the virus's origin, etiology, epidemiology, and clinical symptoms to inform readers more about the Covid-19, eliminate misunderstanding and bias to the virus, invoke the sense of self-protection and finally use scientific and logical methods to overcome this world-wide pandemic.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125207000","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}
J. Martínez-Miranda, Fernando López-Flores, Antonio Palacios-Isaac, Liliana Jiménez, Luis García Medina, Rosa M. Moreno-Robles, Giovanni Rosales
Suicidal behaviour is one of the leading causes of injury and death worldwide. In order to design and implement effective suicide's prevention strategies, it is important the timely identification of individuals at risk, as well as the systematic collection and analysis of suicide-related data. In this paper, we describe the main functionalities of a health information system developed to support general practitioners and mental health specialists with the screening of suicidal behaviours, the management of the follow-up process and the analysis and visualisation of the collected data for surveillance purposes. We also present the initial results obtained after the deployment of the system in six public health care institutions during the first eight months of use.
{"title":"Implementation of a Health Information System to Support the Screening and Surveillance of Suicidal Behaviours","authors":"J. Martínez-Miranda, Fernando López-Flores, Antonio Palacios-Isaac, Liliana Jiménez, Luis García Medina, Rosa M. Moreno-Robles, Giovanni Rosales","doi":"10.1145/3418094.3418108","DOIUrl":"https://doi.org/10.1145/3418094.3418108","url":null,"abstract":"Suicidal behaviour is one of the leading causes of injury and death worldwide. In order to design and implement effective suicide's prevention strategies, it is important the timely identification of individuals at risk, as well as the systematic collection and analysis of suicide-related data. In this paper, we describe the main functionalities of a health information system developed to support general practitioners and mental health specialists with the screening of suicidal behaviours, the management of the follow-up process and the analysis and visualisation of the collected data for surveillance purposes. We also present the initial results obtained after the deployment of the system in six public health care institutions during the first eight months of use.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121932321","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}
Biomedical ontologies contain target domain knowledge. In many cases, multiple ontologies are created independently for different purposes in the same biomedical domain. To fuse and extend existing knowledge, we need to find the corresponding entities (i.e. classes and properties) from different ontologies. Formal Concept Analysis (FCA) is a mature mathematical tool for biomedical ontology matching tasks and has achieved competitive performance. The FCA-based method mainly matches the ontologies through lexical tokens and structural information. This method ignores the inherent semantics of entities. On the other hand, representation learning techniques are widely used in different NLP tasks to capture the semantic similarity of words. In this paper, we propose a novel biomedical ontology matching method which we dub DeepFCA. We use pre-trained word vectors to initialize the vector representations onto which semantic information is inscribed. FCA embedding techniques are used to refine these vectors. DeepFCA combines FCA and word2vec methods to enhance the performance of biomedical ontology matching. To the best of our knowledge, this is the first attempt to apply FCA embedding techniques to biomedical ontology matching. Experiments on real-world biomedical ontologies show that DeepFCA improves the recall and F1-measure compared with the traditional FCA-based algorithm. It also achieves competitive performance compared with several state-of-the-art systems.
{"title":"DeepFCA","authors":"Guoxuan Li","doi":"10.1145/3418094.3418121","DOIUrl":"https://doi.org/10.1145/3418094.3418121","url":null,"abstract":"Biomedical ontologies contain target domain knowledge. In many cases, multiple ontologies are created independently for different purposes in the same biomedical domain. To fuse and extend existing knowledge, we need to find the corresponding entities (i.e. classes and properties) from different ontologies. Formal Concept Analysis (FCA) is a mature mathematical tool for biomedical ontology matching tasks and has achieved competitive performance. The FCA-based method mainly matches the ontologies through lexical tokens and structural information. This method ignores the inherent semantics of entities. On the other hand, representation learning techniques are widely used in different NLP tasks to capture the semantic similarity of words. In this paper, we propose a novel biomedical ontology matching method which we dub DeepFCA. We use pre-trained word vectors to initialize the vector representations onto which semantic information is inscribed. FCA embedding techniques are used to refine these vectors. DeepFCA combines FCA and word2vec methods to enhance the performance of biomedical ontology matching. To the best of our knowledge, this is the first attempt to apply FCA embedding techniques to biomedical ontology matching. Experiments on real-world biomedical ontologies show that DeepFCA improves the recall and F1-measure compared with the traditional FCA-based algorithm. It also achieves competitive performance compared with several state-of-the-art systems.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124752977","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}
Healthcare organisations often adopt electronic health (e-Health) systems with the hope that they can improve the quality and reduce the cost of providing healthcare services. However, literature has shown mixed results concerning the benefits of e-Health systems for healthcare providers. In order for organisations to achieve the benefits of e-Health systems, they need to first adopt and assimilate these technologies into their work practices. Past studies have indicated limited assimilation of e-health systems and have associated it to the complex collaborative nature of clinical workflow processes. This paper presents challenges that inhibit the assimilation of e-Health systems in an emerging economy explored in terms of context and contradictions within the Activity Theory framework. The study explored four healthcare organisations in Nairobi city in Kenya. Results obtained indicated that assimilation is hindered by unresolved contradictions brought about by the interaction between different components in the clinical activity. In order for healthcare organisations to improve the assimilation and subsequent benefits of e-Health systems, they will need to identify and resolve these contradictions.
{"title":"Challenges of Assimilation of e-Health Systems in Healthcare: Insights into Activity Theory","authors":"Patrick Shabaya, I. Ateya, G. Wanyembi","doi":"10.1145/3418094.3418525","DOIUrl":"https://doi.org/10.1145/3418094.3418525","url":null,"abstract":"Healthcare organisations often adopt electronic health (e-Health) systems with the hope that they can improve the quality and reduce the cost of providing healthcare services. However, literature has shown mixed results concerning the benefits of e-Health systems for healthcare providers. In order for organisations to achieve the benefits of e-Health systems, they need to first adopt and assimilate these technologies into their work practices. Past studies have indicated limited assimilation of e-health systems and have associated it to the complex collaborative nature of clinical workflow processes. This paper presents challenges that inhibit the assimilation of e-Health systems in an emerging economy explored in terms of context and contradictions within the Activity Theory framework. The study explored four healthcare organisations in Nairobi city in Kenya. Results obtained indicated that assimilation is hindered by unresolved contradictions brought about by the interaction between different components in the clinical activity. In order for healthcare organisations to improve the assimilation and subsequent benefits of e-Health systems, they will need to identify and resolve these contradictions.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268111","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}
Providing rural residents with effective and affordable health services and financial protection against health risks are more or less problematic for developing countries with massive rural populations. The retrospective payment system (RPS) incurs excessive treatments, causes extreme waste of scarce medical resources and whether the payment system reform by converting to the prospective payment system (RPS) could achieve a desirable triple-win status. In this paper, a county hospital/rural medicare agency evolutionary game theoretical model in NW small-world network with EWA learning model and a corresponding computer model is formulated. We study the diffusion, conversion, and optimization of PPS and RPS, and hospitals' selection of treatments. The results show that PPS itself is a triple-win payment system that could eliminate excessive treatments, but cannot guide hospitals to choose the right intensity of treatments. The conversion from RPS to PPS relies on agencies' strict supervision, hospitals' expectation adjustment speed, and emphasis on future purchaser reimbursement. In order to optimize payment systems, the more hospitals emphasize patients' welfare, the more likely they are to provide appropriate treatments in intensity and moderation. It suggests a further need for developing countries to pursue various payment system reforms from PPS to RPS to pave the way for attaining mutual interests of three parties, especially providing appropriate treatments for rural residents.
{"title":"An Evolutionary Analysis of Hospital Payment System Strategies Based on County Hospitals-Purchasers Game","authors":"Yufei Hu, Lianghua Chen","doi":"10.1145/3418094.3418098","DOIUrl":"https://doi.org/10.1145/3418094.3418098","url":null,"abstract":"Providing rural residents with effective and affordable health services and financial protection against health risks are more or less problematic for developing countries with massive rural populations. The retrospective payment system (RPS) incurs excessive treatments, causes extreme waste of scarce medical resources and whether the payment system reform by converting to the prospective payment system (RPS) could achieve a desirable triple-win status. In this paper, a county hospital/rural medicare agency evolutionary game theoretical model in NW small-world network with EWA learning model and a corresponding computer model is formulated. We study the diffusion, conversion, and optimization of PPS and RPS, and hospitals' selection of treatments. The results show that PPS itself is a triple-win payment system that could eliminate excessive treatments, but cannot guide hospitals to choose the right intensity of treatments. The conversion from RPS to PPS relies on agencies' strict supervision, hospitals' expectation adjustment speed, and emphasis on future purchaser reimbursement. In order to optimize payment systems, the more hospitals emphasize patients' welfare, the more likely they are to provide appropriate treatments in intensity and moderation. It suggests a further need for developing countries to pursue various payment system reforms from PPS to RPS to pave the way for attaining mutual interests of three parties, especially providing appropriate treatments for rural residents.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140188","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}
Pneumonia, a bacterial or viral infection of the lungs that causes the inflammation of the air sacs, is one of the leading causes of mortality of children in the world. Chest x-rays, one of the golden standard tools in determining pneumonia, is mainly used to detect malignancy in the lungs. However, the process of analyzing may be time-consuming for the radiologist, and costly to hospitals. Inter-observer variability with the diagnosis is very high since childhood pneumonia can be difficult to diagnose amongst radiologists. Considering that the design of convolutional neural networks makes it suited to process spatially distributed input such as images, the application of convolutional neural networks trained with chest X-rays to automate the diagnosis of pneumonia is viable. This study evaluates the performance of four well known architectures in literature using a childhood pneumonia dataset: (1) VGGNet, (2) ResNet, (3) DenseNet, and (4) AlexNet. Based on our simulations, VGGNet obtained the highest accuracy and sensitivity, followed by ResNet, which obtained the highest specificity, DenseNet, and AlexNet. Using gradient-weighted class activation to validate the learnt features, we observed that sufficiently deep architectures can effectively learn the features of pneumonia. In addition, the increase in depth improves the information flow at the cost of computational time, which is evident in DenseNet.
{"title":"Performance Evaluation of Convolutional Neural Network Architectures for Diagnosis of Childhood Pneumonia","authors":"Christian Michael C. Qui, P. Abu","doi":"10.1145/3418094.3418120","DOIUrl":"https://doi.org/10.1145/3418094.3418120","url":null,"abstract":"Pneumonia, a bacterial or viral infection of the lungs that causes the inflammation of the air sacs, is one of the leading causes of mortality of children in the world. Chest x-rays, one of the golden standard tools in determining pneumonia, is mainly used to detect malignancy in the lungs. However, the process of analyzing may be time-consuming for the radiologist, and costly to hospitals. Inter-observer variability with the diagnosis is very high since childhood pneumonia can be difficult to diagnose amongst radiologists. Considering that the design of convolutional neural networks makes it suited to process spatially distributed input such as images, the application of convolutional neural networks trained with chest X-rays to automate the diagnosis of pneumonia is viable. This study evaluates the performance of four well known architectures in literature using a childhood pneumonia dataset: (1) VGGNet, (2) ResNet, (3) DenseNet, and (4) AlexNet. Based on our simulations, VGGNet obtained the highest accuracy and sensitivity, followed by ResNet, which obtained the highest specificity, DenseNet, and AlexNet. Using gradient-weighted class activation to validate the learnt features, we observed that sufficiently deep architectures can effectively learn the features of pneumonia. In addition, the increase in depth improves the information flow at the cost of computational time, which is evident in DenseNet.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373152","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}
Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach.
{"title":"A Cloud Based Big Data Health-Analytics-as-a-Service Framework to Support Low Resource Setting Neonatal Intensive Care Unit","authors":"Meghana Bastwadkar, C. McGregor, S. Balaji","doi":"10.1145/3418094.3418130","DOIUrl":"https://doi.org/10.1145/3418094.3418130","url":null,"abstract":"Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072700","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}
COVID19 coronavirus has widely infected more than 10 million people and killed more than 500,000 globally till July 1, 2020. In this paper, we describe a potential methodology, integration of image preprocess, Guided Grad-CAM, machine learning and risk management based on chest radiography images, as one of workable alarm and analysis systems to support clinicians against COVID-19 outbreak threat. We leverage pre-trained CNN models as backbone with further transfer learning to analyze public open datasets composed of 5851 chest radiography images for 4 classes classification, and 15478 images from COVIDx dataset for 3 classes classification, facilitated with steps of ROI and mask, and CNN layer visualization of guided grad-CAM to help CNN focused on critical infection focus in qualitative perspective. In quantitative perspective of 4 classes classification result, accuracy, average sensitivity, average precision, and COVID19 sensitivity of single ResNet50 and our second bagging ensemble model are (77.2%/78.8%/81.9%/100%) and (81.5%/81.4%,86.8%/100%) respectively. Ensemble way of several CNNs and other machine learning methods used here is to contribute about 4% accuracy improvement on top of best single CNN (ResNet50). In our 3 classes classification, those metrics of ensemble model and benchmark are (93.1%/90.1%/89.7%/83%) and (90%/85.9%, 82.4%/77%). We conclude ensemble approach would facilitate weaker classifier more. Beside to accuracy-oriented analysis, a cost minimization approach is suggested here to provide clinicians options of different risk consideration flexibility by trade off among different categories and performance rates.
{"title":"Covid-19 Chest Radiography Images Analysis Based on Integration of Image Preprocess, Guided Grad-CAM, Machine Learning and Risk Management","authors":"Tsung-Chieh Lin, Hsi-Chieh Lee","doi":"10.1145/3418094.3418096","DOIUrl":"https://doi.org/10.1145/3418094.3418096","url":null,"abstract":"COVID19 coronavirus has widely infected more than 10 million people and killed more than 500,000 globally till July 1, 2020. In this paper, we describe a potential methodology, integration of image preprocess, Guided Grad-CAM, machine learning and risk management based on chest radiography images, as one of workable alarm and analysis systems to support clinicians against COVID-19 outbreak threat. We leverage pre-trained CNN models as backbone with further transfer learning to analyze public open datasets composed of 5851 chest radiography images for 4 classes classification, and 15478 images from COVIDx dataset for 3 classes classification, facilitated with steps of ROI and mask, and CNN layer visualization of guided grad-CAM to help CNN focused on critical infection focus in qualitative perspective. In quantitative perspective of 4 classes classification result, accuracy, average sensitivity, average precision, and COVID19 sensitivity of single ResNet50 and our second bagging ensemble model are (77.2%/78.8%/81.9%/100%) and (81.5%/81.4%,86.8%/100%) respectively. Ensemble way of several CNNs and other machine learning methods used here is to contribute about 4% accuracy improvement on top of best single CNN (ResNet50). In our 3 classes classification, those metrics of ensemble model and benchmark are (93.1%/90.1%/89.7%/83%) and (90%/85.9%, 82.4%/77%). We conclude ensemble approach would facilitate weaker classifier more. Beside to accuracy-oriented analysis, a cost minimization approach is suggested here to provide clinicians options of different risk consideration flexibility by trade off among different categories and performance rates.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121347421","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}
The voting process and results and the trust of voters are very important issues in current modern society. When voters could trust that the voting results are valid, people are willing to trust this voter will serve for them dealing with issues such as national security, education, and economics in the future. Electronic voting is an efficient method that could help people who could not vote in a limited period. Besides, it also solves the problem of long voting time due to many voters or more complicated voting steps. Although the voting machine could perform a record of votes and voting calculations, it does not require humans to handle a lot of above voting works. Nowadays, electronic voting has become an important research. However, there are too many assumptions and institutions which are often added in this research area. It might become to be unpractical when this voting research is applied to real society. Hence, we thought that blockchain might be a good solution for electronic voting research. Nevertheless, how to apply blockchain to protect people's privacy, anonymity, and voting rights still needs to be discussed in current days. In this paper, we proposed cryptanalysis on a trustworthy electronic voting scheme with blockchain and find out that there are some problems in their proposed scheme.
{"title":"A Cryptanalysis of Trustworthy Electronicvoting using Adjusted Blockchain Technology","authors":"Ming-Te Chen, C. Chen, Tsung-Hung Lin","doi":"10.1145/3418094.3418143","DOIUrl":"https://doi.org/10.1145/3418094.3418143","url":null,"abstract":"The voting process and results and the trust of voters are very important issues in current modern society. When voters could trust that the voting results are valid, people are willing to trust this voter will serve for them dealing with issues such as national security, education, and economics in the future. Electronic voting is an efficient method that could help people who could not vote in a limited period. Besides, it also solves the problem of long voting time due to many voters or more complicated voting steps. Although the voting machine could perform a record of votes and voting calculations, it does not require humans to handle a lot of above voting works. Nowadays, electronic voting has become an important research. However, there are too many assumptions and institutions which are often added in this research area. It might become to be unpractical when this voting research is applied to real society. Hence, we thought that blockchain might be a good solution for electronic voting research. Nevertheless, how to apply blockchain to protect people's privacy, anonymity, and voting rights still needs to be discussed in current days. In this paper, we proposed cryptanalysis on a trustworthy electronic voting scheme with blockchain and find out that there are some problems in their proposed scheme.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125187716","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}
Chaitawat Sa-ngamuang, P. Haddawy, S. Lawpoolsri, T. Barkowsky, Patiwat Sa-angchai
Malaria elimination remains a major challenge worldwide largely because human mobility can result in importing cases from areas of high incidence to areas of low incidence. Thus, understanding the role of human mobility in malaria transmission is essential. In this study, we collect mobility data from 88 participants over ten months using a smartphone application. Our study area is in northern Thailand along the border with Myanmar, from which malaria may be imported. We analyze amount of time spent in Thailand/Myanmar in areas of various land cover types, spatial distribution of movement, and network patterns of movement. We find significant differences between villages in amounts of time spent in forest areas and in Myanmar, with most travel to Myanmar occurring from two villages. We find significantly higher spatial distribution of movement in the dry season than the wet season. Our results provide important insight to help target surveillance and intervention.
{"title":"A Study of Individual Human Mobility Patterns Related to Malaria Transmission Along the Thai-Myanmar Border","authors":"Chaitawat Sa-ngamuang, P. Haddawy, S. Lawpoolsri, T. Barkowsky, Patiwat Sa-angchai","doi":"10.1145/3418094.3418136","DOIUrl":"https://doi.org/10.1145/3418094.3418136","url":null,"abstract":"Malaria elimination remains a major challenge worldwide largely because human mobility can result in importing cases from areas of high incidence to areas of low incidence. Thus, understanding the role of human mobility in malaria transmission is essential. In this study, we collect mobility data from 88 participants over ten months using a smartphone application. Our study area is in northern Thailand along the border with Myanmar, from which malaria may be imported. We analyze amount of time spent in Thailand/Myanmar in areas of various land cover types, spatial distribution of movement, and network patterns of movement. We find significant differences between villages in amounts of time spent in forest areas and in Myanmar, with most travel to Myanmar occurring from two villages. We find significantly higher spatial distribution of movement in the dry season than the wet season. Our results provide important insight to help target surveillance and intervention.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116730280","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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