P. Bruha, R. Mouček, P. Volf, L. Simeckové, O. Šťáva
They are many risk factors decreasing overall human physical and cognitive performance and increasing incidence of chronic diseases. It is very beneficial for any society to map, discuss and cope with these factors. This can be supported and evaluated by designing, developing, testing and using suitable self-management health systems. One of these systems is the BodyInNumbers exercise and wellness health strategy framework that allows experimenters to collect various heterogeneous health related data in a highly organized and efficient way. Thanks to its success and daily use, new requirements related to better security, scalability and maintainability of its architecture have emerged. The aim of this work is to present advances and changes in the architecture of the BodyInNumbers health strategy framework mainly focusing on new definition of user roles, optimization of the system deployment, and orchestration of the system components. As a proof of concept, a Kubernetes cluster prototype has been used to demonstrate the improved architectural solution.
{"title":"On Architecture of BodyInNumbers Exercise and Wellness Health Strategy Framework","authors":"P. Bruha, R. Mouček, P. Volf, L. Simeckové, O. Šťáva","doi":"10.1145/3418094.3418102","DOIUrl":"https://doi.org/10.1145/3418094.3418102","url":null,"abstract":"They are many risk factors decreasing overall human physical and cognitive performance and increasing incidence of chronic diseases. It is very beneficial for any society to map, discuss and cope with these factors. This can be supported and evaluated by designing, developing, testing and using suitable self-management health systems. One of these systems is the BodyInNumbers exercise and wellness health strategy framework that allows experimenters to collect various heterogeneous health related data in a highly organized and efficient way. Thanks to its success and daily use, new requirements related to better security, scalability and maintainability of its architecture have emerged. The aim of this work is to present advances and changes in the architecture of the BodyInNumbers health strategy framework mainly focusing on new definition of user roles, optimization of the system deployment, and orchestration of the system components. As a proof of concept, a Kubernetes cluster prototype has been used to demonstrate the improved architectural solution.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"63 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":"127026073","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}
Xinyue Liang, Qinneng Xu, Ruo-ping Guan, Yang Zhao
Background: Tuberculosis is a common infectious disease primarily targeting the lungs and of high morality and prevalence. Efficient prediction of tuberculosis is important to counter epidemics and successfully allocate recourse. This study's main objective is to investigate the effectiveness of using web search queries in predicting the incidence of tuberculosis in China. We conduct a comprehensive comparison on data driven methods for predicting the incidence of tuberculosis. Methods: Several data mining models are implemented in our study, including stepwise linear regression and SVM incorporating Baidu index (a recording of search queries on Baidu, the main search engine in China). The two methods are compared with traditional time series methods of autoregressive integrated moving (ARIMA) and seasonal ARIMA (SARIMA). In addition, to further investigate the reliability of prediction, the effectiveness of integrating the individual models is explored in our study, a hybrid model of SARIMA and SVM and Bayesian model averaging (BMA) are adopted to maximize the predictive utility of the models. Results and Conclusion: The experiment results show that Internet queries provide effective data sources for predicting tuberculosis, with comparable predicting ability to that of traditional time series models. It also shows that combining two or models using BMA or hybrid models can improve the prediction ability, with BMA showing by far the best results in prediction in terms of both MAPE and RSME in the 5 areas studied (Guangdong, Beijing, Tianjin and Shanghai). The findings from this study pave the way for developing accurate and timely prediction of tuberculosis cases, which is important for allocating healthcare recourses and developing strategies to counter possible future outbreaks in real practice.
{"title":"Forecasting Tuberculosis Incidence in China using Baidu Index: A Comparative Study","authors":"Xinyue Liang, Qinneng Xu, Ruo-ping Guan, Yang Zhao","doi":"10.1145/3418094.3418129","DOIUrl":"https://doi.org/10.1145/3418094.3418129","url":null,"abstract":"Background: Tuberculosis is a common infectious disease primarily targeting the lungs and of high morality and prevalence. Efficient prediction of tuberculosis is important to counter epidemics and successfully allocate recourse. This study's main objective is to investigate the effectiveness of using web search queries in predicting the incidence of tuberculosis in China. We conduct a comprehensive comparison on data driven methods for predicting the incidence of tuberculosis. Methods: Several data mining models are implemented in our study, including stepwise linear regression and SVM incorporating Baidu index (a recording of search queries on Baidu, the main search engine in China). The two methods are compared with traditional time series methods of autoregressive integrated moving (ARIMA) and seasonal ARIMA (SARIMA). In addition, to further investigate the reliability of prediction, the effectiveness of integrating the individual models is explored in our study, a hybrid model of SARIMA and SVM and Bayesian model averaging (BMA) are adopted to maximize the predictive utility of the models. Results and Conclusion: The experiment results show that Internet queries provide effective data sources for predicting tuberculosis, with comparable predicting ability to that of traditional time series models. It also shows that combining two or models using BMA or hybrid models can improve the prediction ability, with BMA showing by far the best results in prediction in terms of both MAPE and RSME in the 5 areas studied (Guangdong, Beijing, Tianjin and Shanghai). The findings from this study pave the way for developing accurate and timely prediction of tuberculosis cases, which is important for allocating healthcare recourses and developing strategies to counter possible future outbreaks in real practice.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"23 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":"126556126","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}
Harnessing geospatial technologies (GPS, GIS, internet mapping and remote sensing) can promote collaboration of humans, environmental, and healthcare resources-for real-time decision support in emergency healthcare scenarios. Oftentimes, high death rates are recorded by emergency rescue workers due to infrastructural deficit and sparsity of ambulatory services within the patients' location. Also, acute shortage of health personnel may pose great risk to patients requiring emergency healthcare services, as limited knowledge of healthcare data can prevent proactive, just-in-time response to emergency, and dearth of appropriate healthcare policies. This study therefore proposes a geospatial recommender framework that connects patients with healthcare providers and other relevant stakeholders; to exploit and filter in context, emergency healthcare information and knowledge-for enhanced response strategy that minimizes fatalities among patients. We examine case study data within the African context and discuss infrastructural challenges, opportunities and design implications, to demonstrate the feasibility of our framework. The immediate benefit of the framework is enhanced specialty care for home-bound patients or in emergency cases where distance appears prohibitive. Our strategy will certainly scale up complex healthcare interventions to large populations, as a new paradigm for evidence-based intervention with state-of-the-art technology for optimized services is guaranteed.
{"title":"Improving Emergency Healthcare Response using Real-Time Collaborative Technology","authors":"Ifiok J. Udo, M. Ekpenyong","doi":"10.1145/3418094.3418118","DOIUrl":"https://doi.org/10.1145/3418094.3418118","url":null,"abstract":"Harnessing geospatial technologies (GPS, GIS, internet mapping and remote sensing) can promote collaboration of humans, environmental, and healthcare resources-for real-time decision support in emergency healthcare scenarios. Oftentimes, high death rates are recorded by emergency rescue workers due to infrastructural deficit and sparsity of ambulatory services within the patients' location. Also, acute shortage of health personnel may pose great risk to patients requiring emergency healthcare services, as limited knowledge of healthcare data can prevent proactive, just-in-time response to emergency, and dearth of appropriate healthcare policies. This study therefore proposes a geospatial recommender framework that connects patients with healthcare providers and other relevant stakeholders; to exploit and filter in context, emergency healthcare information and knowledge-for enhanced response strategy that minimizes fatalities among patients. We examine case study data within the African context and discuss infrastructural challenges, opportunities and design implications, to demonstrate the feasibility of our framework. The immediate benefit of the framework is enhanced specialty care for home-bound patients or in emergency cases where distance appears prohibitive. Our strategy will certainly scale up complex healthcare interventions to large populations, as a new paradigm for evidence-based intervention with state-of-the-art technology for optimized services is guaranteed.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"103 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":"128879908","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}
Exercise ECG is currently the best way for diagnosing heart disease, but it is not suitable for everyone. This study used data mining to establish a model to predict the risk of heart disease. Maximum oxygen uptake (VO2max) was used as an indicator of determine that the person was a high-risk or low-risk heart disease patient. Data of the National Health and Nutrition Examination Survey from the United States were used in this study. Due to scattered distribution of the data, which diminished the prediction performance, this study proposed a novel method to stratify data with the propensity scores. The subsets of data were trained by the support vector machine to establish the prediction model. The results of this study showed that the model had an AUC of 0.899. Our model can make a more accurate prediction to identify whether a patient has a higher risk in heart disease.
{"title":"Applying Propensity Score and Support Vector Machine to Construct a Predictive Model for Heart Disease","authors":"Hsueh-Yi Lu","doi":"10.1145/3418094.3418117","DOIUrl":"https://doi.org/10.1145/3418094.3418117","url":null,"abstract":"Exercise ECG is currently the best way for diagnosing heart disease, but it is not suitable for everyone. This study used data mining to establish a model to predict the risk of heart disease. Maximum oxygen uptake (VO2max) was used as an indicator of determine that the person was a high-risk or low-risk heart disease patient. Data of the National Health and Nutrition Examination Survey from the United States were used in this study. Due to scattered distribution of the data, which diminished the prediction performance, this study proposed a novel method to stratify data with the propensity scores. The subsets of data were trained by the support vector machine to establish the prediction model. The results of this study showed that the model had an AUC of 0.899. Our model can make a more accurate prediction to identify whether a patient has a higher risk in heart disease.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"44 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":"131587078","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}
M. Mamani, M. Chauca, E. Huamani, Richardo Gonzales
Objective: To determine the risk factors associated with extensively multidrug-resistant tuberculosis in multidrug-resistant patients in the DIRIS Lima Sur, 2017. Type and design: The type of research was observational and analytical, retrospective case and control. The population consisted of a total of 158 patients who received treatment for MDR-TB between 2016 and 2017. The sample consisted of 24 cases diagnosed with extensively resistant pulmonary tuberculosis (XDR-TB) and 48 controls with MDR-TB. The data collection technique was the survey and the instrument was a questionnaire for both groups (case-controls). Results: 5 internal and external risk factors associated with XDR-TB were identified. Among the internal factors associated at the bivariate level were: drug consumption, having received TB/MDR-TB treatment previously, having received TB/MDR-TB treatment less than one year, having failed to the primary and individualized scheme for TB/MDR-TB (p<0.05); and the multivariate analysis confirmed the influence of the factor "having failed to the primary and individualized scheme for TB/MDR-TB". Among the most associated external factors at the multivariate level were: having relatives who died of XDR/TB-MDR and having neighbors or friends in the neighborhood who died of TB (p<0.05). Conclusion: It was concluded that the factors: "failure to the primary and individualized scheme for TB-MDR-TB" and "having relatives who died of XDR-TB-MDR-TB" are of risk associated with the presence of extensively resistant TB, controlling the effect of risk factors prevents the development of TB.
{"title":"Characterization of Risk Factors Associated with Extensively Multidrug-Resistant Tuberculosis in Public Health Institutions","authors":"M. Mamani, M. Chauca, E. Huamani, Richardo Gonzales","doi":"10.1145/3418094.3418099","DOIUrl":"https://doi.org/10.1145/3418094.3418099","url":null,"abstract":"Objective: To determine the risk factors associated with extensively multidrug-resistant tuberculosis in multidrug-resistant patients in the DIRIS Lima Sur, 2017. Type and design: The type of research was observational and analytical, retrospective case and control. The population consisted of a total of 158 patients who received treatment for MDR-TB between 2016 and 2017. The sample consisted of 24 cases diagnosed with extensively resistant pulmonary tuberculosis (XDR-TB) and 48 controls with MDR-TB. The data collection technique was the survey and the instrument was a questionnaire for both groups (case-controls). Results: 5 internal and external risk factors associated with XDR-TB were identified. Among the internal factors associated at the bivariate level were: drug consumption, having received TB/MDR-TB treatment previously, having received TB/MDR-TB treatment less than one year, having failed to the primary and individualized scheme for TB/MDR-TB (p<0.05); and the multivariate analysis confirmed the influence of the factor \"having failed to the primary and individualized scheme for TB/MDR-TB\". Among the most associated external factors at the multivariate level were: having relatives who died of XDR/TB-MDR and having neighbors or friends in the neighborhood who died of TB (p<0.05). Conclusion: It was concluded that the factors: \"failure to the primary and individualized scheme for TB-MDR-TB\" and \"having relatives who died of XDR-TB-MDR-TB\" are of risk associated with the presence of extensively resistant TB, controlling the effect of risk factors prevents the development of TB.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"77 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":"127436554","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}
Luis E Huaman, J. Ramon, Ivonne Jara, Humberto Cancho, Flordelis C. Reyes, M. Chauca
Faced with pandemic events, health personnel on the front line of care for seriously ill patients with COVID-19 infection, turns out to be vulnerable to mental health problems. Objective: The objective of this review study is to identify the psychological effects of the Coronavirus pandemic on hospital health personnel and the different scales used to assess the psychological impact. Methodology: A systematic literature review was carried out on five search platforms such as PubMed, Elsevier, The Lancet, Google academic and Scielo. Then, apply selection criteria and methodological quality checklists, take 16 articles for data extraction and analysis. Results: All the recognized studies recognize the development of symptoms related to the mental health of the workers who are facing the care of patients with COVID-19, highlighting depression, anxiety and post traumatic stress as effects of greater impact. On the other hand, a diversity of scales used in the articles analyzed was found, the Post-Traumatic Test and the Revised Event Impact Scale standing out. Conclusion: The pandemic caused by COVID-19 is having a negative impact on the mental health of health personnel and there are a variety of validated scales to measure the psychological effect.
{"title":"Psychological Effect of the COVID 19 Pandemic on Hospital Health Personnel based on Impact Scales","authors":"Luis E Huaman, J. Ramon, Ivonne Jara, Humberto Cancho, Flordelis C. Reyes, M. Chauca","doi":"10.1145/3418094.3418100","DOIUrl":"https://doi.org/10.1145/3418094.3418100","url":null,"abstract":"Faced with pandemic events, health personnel on the front line of care for seriously ill patients with COVID-19 infection, turns out to be vulnerable to mental health problems. Objective: The objective of this review study is to identify the psychological effects of the Coronavirus pandemic on hospital health personnel and the different scales used to assess the psychological impact. Methodology: A systematic literature review was carried out on five search platforms such as PubMed, Elsevier, The Lancet, Google academic and Scielo. Then, apply selection criteria and methodological quality checklists, take 16 articles for data extraction and analysis. Results: All the recognized studies recognize the development of symptoms related to the mental health of the workers who are facing the care of patients with COVID-19, highlighting depression, anxiety and post traumatic stress as effects of greater impact. On the other hand, a diversity of scales used in the articles analyzed was found, the Post-Traumatic Test and the Revised Event Impact Scale standing out. Conclusion: The pandemic caused by COVID-19 is having a negative impact on the mental health of health personnel and there are a variety of validated scales to measure the psychological effect.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"155 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":"115286872","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}
Salah Afraa, Hashim Raghad, S. Shaymaa, Khalid Ayam, H. Aeshah
Background: in December, 2019 corona virus discovered in Wuhan, china as a new viral infection affect respiratory system. It is then distributed throughout the whole word causing death and sickness for huge number of people in most of the countries. This study evaluate Ajman university dental professionals' knowledge and attitudes towards corona virus and the applied strategies for its prevention and treatment. Material and methods: The study done by online questionnaire which distributed to the participants through emails. The questionnaire consists of three parts (demographic, knowledge, and attitudes) towards corona virus which was filled by 297 dentists. Data analyzed by (SPSS, 22V) to revealed study results. Results: study showed that (100%) know the main rout of virus transmission through close contact with the patient and sharing things with patients. However, there is a loss of information towards other way of virus transmission. (78%) of the participants do not know about any medication for corona virus nor a vaccination for it. (76%) they are willing to treat patients with corona however they prefer to do it in hospitals or specialized centers. (95%) of the participants ask for more precautions and preventive measures for dental treatment of corona virus patients. The majority of dental professionals had anxiety towards dental treatment of corona virus. Conclusions: study findings revealed that dental professionals showed good knowledge towards virus transmission, however there are missing information of other ways of transmission. This finding indicates that providing information strategies by health institutions should be updated to enhance information needed for the medical staff. Study showed that infection control should be applied in all dental clinics and high quality of dental materials and instruments for preventive measures should be provided. To avoid dental staff anxiety towards dental treatment of corona virus patients' high standard staff preparation should be provided during dental treatment.
{"title":"Knowledge and Attitudes of Ajman University Dentists towards the Coronavirus (COVID-19)","authors":"Salah Afraa, Hashim Raghad, S. Shaymaa, Khalid Ayam, H. Aeshah","doi":"10.1145/3418094.3418097","DOIUrl":"https://doi.org/10.1145/3418094.3418097","url":null,"abstract":"Background: in December, 2019 corona virus discovered in Wuhan, china as a new viral infection affect respiratory system. It is then distributed throughout the whole word causing death and sickness for huge number of people in most of the countries. This study evaluate Ajman university dental professionals' knowledge and attitudes towards corona virus and the applied strategies for its prevention and treatment. Material and methods: The study done by online questionnaire which distributed to the participants through emails. The questionnaire consists of three parts (demographic, knowledge, and attitudes) towards corona virus which was filled by 297 dentists. Data analyzed by (SPSS, 22V) to revealed study results. Results: study showed that (100%) know the main rout of virus transmission through close contact with the patient and sharing things with patients. However, there is a loss of information towards other way of virus transmission. (78%) of the participants do not know about any medication for corona virus nor a vaccination for it. (76%) they are willing to treat patients with corona however they prefer to do it in hospitals or specialized centers. (95%) of the participants ask for more precautions and preventive measures for dental treatment of corona virus patients. The majority of dental professionals had anxiety towards dental treatment of corona virus. Conclusions: study findings revealed that dental professionals showed good knowledge towards virus transmission, however there are missing information of other ways of transmission. This finding indicates that providing information strategies by health institutions should be updated to enhance information needed for the medical staff. Study showed that infection control should be applied in all dental clinics and high quality of dental materials and instruments for preventive measures should be provided. To avoid dental staff anxiety towards dental treatment of corona virus patients' high standard staff preparation should be provided during dental treatment.","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":"128748929","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 blockchain technology designed by information security experts empowers in library service and healthcare service via smart contracts. Advanced blockchain technology provides a mechanism for smart contracts. Through intelligent threshold design and the open and tamper-proof nature of the blockchain, smart contracts have become a new generation of information security technology. In the next-generation library services, traditional borrowing materials from the library have evolved into a limited-time sharing service for electronic materials. These electronic materials have various loan periods and sharing time-varying by different borrower privileges. This study designs a patron-centric account's smart contract to disseminate library information more effectively and collect accurate library data. The smart contract automatically triggers the loan period and extends this mechanism to designing a personal health privacy sharing protocol for improving the one-time authorization sharing mechanism. As a result, this study may provide blockchain smart contract designs shared by library information and healthcare through cross-domain consideration and scheme.
{"title":"Cross-domain Design of Blockchain Smart Contract for Library and Healthcare Privacy","authors":"Y. Kuo, J. Shieh","doi":"10.1145/3418094.3418145","DOIUrl":"https://doi.org/10.1145/3418094.3418145","url":null,"abstract":"The blockchain technology designed by information security experts empowers in library service and healthcare service via smart contracts. Advanced blockchain technology provides a mechanism for smart contracts. Through intelligent threshold design and the open and tamper-proof nature of the blockchain, smart contracts have become a new generation of information security technology. In the next-generation library services, traditional borrowing materials from the library have evolved into a limited-time sharing service for electronic materials. These electronic materials have various loan periods and sharing time-varying by different borrower privileges. This study designs a patron-centric account's smart contract to disseminate library information more effectively and collect accurate library data. The smart contract automatically triggers the loan period and extends this mechanism to designing a personal health privacy sharing protocol for improving the one-time authorization sharing mechanism. As a result, this study may provide blockchain smart contract designs shared by library information and healthcare through cross-domain consideration and scheme.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"80 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":"122854285","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}
Karan Bhanot, Dominic Schroeder, Isaac Llewellyn, Nicholas Luczak, Thilanka Munasinghe
Mosquitoes are responsible for transfer of many vector-borne diseases including Malaria, Zika and Dengue. These amount to 17% of the total infectious diseases across the globe, leading to a death toll approximately 700,000 annually. Dengue is a preventable viral infection transmitted by Aedes mosquitoes. However, over the past 50 years, the number of dengue cases has increased by a whopping 30-fold. Every year an approximately 500,000 people are admitted with severe dengue, with an estimated 40,000 deaths. In several countries in south American continent and Asia, dengue is one of the leading causes of death. It is mainly found in tropical and sub-tropical regions, particularly surrounding urban and semi-urban areas. Historically, there has been an intensive increase in the number of dengue cases from 2000-2010 and, if adequately explored, essential information can be retrieved. Our work involves the development of the Dengue Spread Information System (DSIS), a geographic-health information system designed to highlight the spread of dengue cases in Iquitos, Peru, and San Juan, Puerto Rico from 1990 to 2013. The application is aimed at citizens, travelers, policymakers and researchers to analyze and interpret the change in risk factors leading to dengue outbreaks and develop essential early warning applications and policies to counter future dengue outbreaks.
{"title":"Dengue Spread Information System (DSIS)","authors":"Karan Bhanot, Dominic Schroeder, Isaac Llewellyn, Nicholas Luczak, Thilanka Munasinghe","doi":"10.1145/3418094.3418133","DOIUrl":"https://doi.org/10.1145/3418094.3418133","url":null,"abstract":"Mosquitoes are responsible for transfer of many vector-borne diseases including Malaria, Zika and Dengue. These amount to 17% of the total infectious diseases across the globe, leading to a death toll approximately 700,000 annually. Dengue is a preventable viral infection transmitted by Aedes mosquitoes. However, over the past 50 years, the number of dengue cases has increased by a whopping 30-fold. Every year an approximately 500,000 people are admitted with severe dengue, with an estimated 40,000 deaths. In several countries in south American continent and Asia, dengue is one of the leading causes of death. It is mainly found in tropical and sub-tropical regions, particularly surrounding urban and semi-urban areas. Historically, there has been an intensive increase in the number of dengue cases from 2000-2010 and, if adequately explored, essential information can be retrieved. Our work involves the development of the Dengue Spread Information System (DSIS), a geographic-health information system designed to highlight the spread of dengue cases in Iquitos, Peru, and San Juan, Puerto Rico from 1990 to 2013. The application is aimed at citizens, travelers, policymakers and researchers to analyze and interpret the change in risk factors leading to dengue outbreaks and develop essential early warning applications and policies to counter future dengue outbreaks.","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":"122710520","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}
This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.
{"title":"Disease Predictive Modeling for Healthcare Management System","authors":"Khulood Nakhat, Fatima Khalique, S. Khan","doi":"10.1145/3418094.3418134","DOIUrl":"https://doi.org/10.1145/3418094.3418134","url":null,"abstract":"This study attempts to perform predictive analytics for decision makers in healthcare management systems using surveillance data from multiple sources for formulating intervention programs based on the results. With the availability of big data in health from multiple sources including electronic health records, it is possible to integrate data and perform near real-time predictive analysis for incoming streams of disease incidences. We use a temporal predictive Auto-Regressive Integrated Moving Averaging model (ARIMA) in combination with a minimum size moving window to forecast the disease incidences over a data collection and integration framework. We applied our model for predictive analysis of Hepatitis C incidences in Vehari District of Punjab province in Pakistan. Model performance is evaluated based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model is capable of finding trends of any disease to aid timely decision making in the healthcare management context.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"31 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":"128235270","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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