2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...最新文献
Pub Date : 2020-08-01DOI: 10.1109/IRI49571.2020.00035
J. Cimino, Wayne H. Liang, Jelai Wang, Dongmei Sun, J. D. Osborne, Amy Y. Wang, S. L. Bridges, Matt C. Wyatt, J. Chen
In response to a need for diverse computing support for translational science teams, the Informatics Institute at the University of Alabama at Birmingham (UAB) has developed a prototype platform called UAB Biomedical Research Infrastructure Technology Enhancement (U-BRITE). This platform provides project management functionality, high-volume data storage, access to clinical data, processing of data through custom pipelines, and high-performance computing in an environment that is compliant with privacy regulations. The project was designed and developed with the help of four biomedical sciences teams, each with their own -omics data, clinical data, and research questions. This paper describes U-BRITE’s architecture (accessible at https://ubrite.org/) and the experience of the members of four teams who were its initial users. Our experience provides useful guidance for future data reuse and an open science model of collaborative biomedical research.
{"title":"Empowering Team Science Across the Translational Spectrum with the UAB Biomedical Research Infrastructure Technology Enhancement (U-BRITE)","authors":"J. Cimino, Wayne H. Liang, Jelai Wang, Dongmei Sun, J. D. Osborne, Amy Y. Wang, S. L. Bridges, Matt C. Wyatt, J. Chen","doi":"10.1109/IRI49571.2020.00035","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00035","url":null,"abstract":"In response to a need for diverse computing support for translational science teams, the Informatics Institute at the University of Alabama at Birmingham (UAB) has developed a prototype platform called UAB Biomedical Research Infrastructure Technology Enhancement (U-BRITE). This platform provides project management functionality, high-volume data storage, access to clinical data, processing of data through custom pipelines, and high-performance computing in an environment that is compliant with privacy regulations. The project was designed and developed with the help of four biomedical sciences teams, each with their own -omics data, clinical data, and research questions. This paper describes U-BRITE’s architecture (accessible at https://ubrite.org/) and the experience of the members of four teams who were its initial users. Our experience provides useful guidance for future data reuse and an open science model of collaborative biomedical research.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"9 1","pages":"194-200"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84144146","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 : 2020-07-18DOI: 10.1109/IRI49571.2020.00075
Gabriel Mersy, Vincent Santore, Isaac Rand, Corrine Kleinman, Grant Wilson, Jason Bonsall, Tyler Edwards
We present a novel approach to the measurement of American state legislature polarization with an experimental comparison of three different machine learning algorithms. Our approach strictly relies on public data sources and open source software. The results suggest that artificial neural network regression has the best outcome compared to both support vector machine and ordinary least squares regression in the prediction of both state House and state Senate legislature polarization. In addition to the technical outcomes of our study, broader implications are assessed as a means of highlighting the importance of accessible information for the higher purpose of promoting civic responsibility.
{"title":"A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization","authors":"Gabriel Mersy, Vincent Santore, Isaac Rand, Corrine Kleinman, Grant Wilson, Jason Bonsall, Tyler Edwards","doi":"10.1109/IRI49571.2020.00075","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00075","url":null,"abstract":"We present a novel approach to the measurement of American state legislature polarization with an experimental comparison of three different machine learning algorithms. Our approach strictly relies on public data sources and open source software. The results suggest that artificial neural network regression has the best outcome compared to both support vector machine and ordinary least squares regression in the prediction of both state House and state Senate legislature polarization. In addition to the technical outcomes of our study, broader implications are assessed as a means of highlighting the importance of accessible information for the higher purpose of promoting civic responsibility.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"20 1","pages":"451-456"},"PeriodicalIF":0.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82677134","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}
Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events. Therefore, early identification of AKI may improve renal function recovery, decrease comorbidities, and further improve patients’ survival. To control certain risk factors and develop targeted prevention strategies are important to reduce the risk of AKI. Drug-drug interactions and drug-disease interactions are critical issues for AKI. Typical statistical approaches cannot handle the complexity of drug-drug and drug-disease interactions. In this paper, we propose a novel learning algorithm, Deep Rule Forests (DRF), which discovers rules from multilayer tree models as the combinations of drug usages and disease indications to help identify such interactions. We found that several disease and drug usages are considered having significant impact on the occurrence of AKI. Our experimental results also show that the DRF model performs comparatively better than typical tree-based and other state-of-the-art algorithms in terms of prediction accuracy and model interpretability.
{"title":"Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests","authors":"Bowen Kuo, Yihuang Kang, Pinghsung Wu, Sheng-Tai Huang, Yajie Huang","doi":"10.1109/IRI49571.2020.00062","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00062","url":null,"abstract":"Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events. Therefore, early identification of AKI may improve renal function recovery, decrease comorbidities, and further improve patients’ survival. To control certain risk factors and develop targeted prevention strategies are important to reduce the risk of AKI. Drug-drug interactions and drug-disease interactions are critical issues for AKI. Typical statistical approaches cannot handle the complexity of drug-drug and drug-disease interactions. In this paper, we propose a novel learning algorithm, Deep Rule Forests (DRF), which discovers rules from multilayer tree models as the combinations of drug usages and disease indications to help identify such interactions. We found that several disease and drug usages are considered having significant impact on the occurrence of AKI. Our experimental results also show that the DRF model performs comparatively better than typical tree-based and other state-of-the-art algorithms in terms of prediction accuracy and model interpretability.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"37 1","pages":"385-390"},"PeriodicalIF":0.0,"publicationDate":"2020-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81411727","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 : 2020-03-16DOI: 10.1109/IRI49571.2020.00033
H. Maghdid, K. Ghafoor, A. Sadiq, K. Curran, Khaled Maaiuf Rabie
Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.
{"title":"A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study","authors":"H. Maghdid, K. Ghafoor, A. Sadiq, K. Curran, Khaled Maaiuf Rabie","doi":"10.1109/IRI49571.2020.00033","DOIUrl":"https://doi.org/10.1109/IRI49571.2020.00033","url":null,"abstract":"Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"27 1","pages":"180-187"},"PeriodicalIF":0.0,"publicationDate":"2020-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81474119","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}
2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：