Pub Date : 2020-04-12DOI: 10.1007/s41666-020-00068-2
Taiyu Zhu, Kezhi Li, Jianwei Chen, P. Herrero, P. Georgiou
{"title":"Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes","authors":"Taiyu Zhu, Kezhi Li, Jianwei Chen, P. Herrero, P. Georgiou","doi":"10.1007/s41666-020-00068-2","DOIUrl":"https://doi.org/10.1007/s41666-020-00068-2","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2020-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-020-00068-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44218210","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 : 2020-03-02DOI: 10.1007/s41666-020-00069-1
Sebastian Daberdaku, E. Tavazzi, B. Di Camillo
{"title":"A Combined Interpolation and Weighted K-Nearest Neighbours Approach for the Imputation of Longitudinal ICU Laboratory Data","authors":"Sebastian Daberdaku, E. Tavazzi, B. Di Camillo","doi":"10.1007/s41666-020-00069-1","DOIUrl":"https://doi.org/10.1007/s41666-020-00069-1","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2020-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-020-00069-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43945220","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 : 2020-01-22DOI: 10.1007/s41666-019-00065-0
J. Weber, J. Ho
{"title":"Applying Bidirectional Transformations to the Design of Interoperable EMR Systems","authors":"J. Weber, J. Ho","doi":"10.1007/s41666-019-00065-0","DOIUrl":"https://doi.org/10.1007/s41666-019-00065-0","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2020-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-019-00065-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53225032","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 : 2020-01-01Epub Date: 2020-06-08DOI: 10.1007/s41666-020-00074-4
{"title":"Editor's Note: <i>Journal of Healthcare Informatics Research</i> and COVID-19 Research.","authors":"","doi":"10.1007/s41666-020-00074-4","DOIUrl":"https://doi.org/10.1007/s41666-020-00074-4","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-020-00074-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38399058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01Epub Date: 2020-11-12DOI: 10.1007/s41666-020-00080-6
Mohammad Nasajpour, Seyedamin Pouriyeh, Reza M Parizi, Mohsen Dorodchi, Maria Valero, Hamid R Arabnia
In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.
{"title":"Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study.","authors":"Mohammad Nasajpour, Seyedamin Pouriyeh, Reza M Parizi, Mohsen Dorodchi, Maria Valero, Hamid R Arabnia","doi":"10.1007/s41666-020-00080-6","DOIUrl":"https://doi.org/10.1007/s41666-020-00080-6","url":null,"abstract":"<p><p>In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.</p>","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-020-00080-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38613633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-18DOI: 10.1007/s41666-019-00061-4
Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
{"title":"DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders","authors":"Jacob Nogas, Shehroz S. Khan, Alex Mihailidis","doi":"10.1007/s41666-019-00061-4","DOIUrl":"https://doi.org/10.1007/s41666-019-00061-4","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2019-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-019-00061-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53224940","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}
{"title":"Transfer Learning for Clinical Time Series Analysis Using Deep Neural Networks.","authors":"Priyanka Gupta, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff","doi":"10.1007/s41666-019-00062-3","DOIUrl":"10.1007/s41666-019-00062-3","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48476858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-19DOI: 10.1007/s41666-019-00058-z
D. Luong, Prerna Singh, Mahin Ramezani, V. Chandola
{"title":"longSil: an Evaluation Metric to Assess Quality of Clustering Longitudinal Clinical Data","authors":"D. Luong, Prerna Singh, Mahin Ramezani, V. Chandola","doi":"10.1007/s41666-019-00058-z","DOIUrl":"https://doi.org/10.1007/s41666-019-00058-z","url":null,"abstract":"","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2019-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41666-019-00058-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44768284","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 : 2019-09-01Epub Date: 2019-01-10DOI: 10.1007/s41666-018-0043-8
Na Hong, Kui Wang, Sizhu Wu, Feichen Shen, Lixia Yao, Guoqian Jiang
The rich semantic representation and sophisticated structure definition of the HL7 Fast Healthcare Interoperability Resources (FHIR) specification requires relatively great efforts to understand and utilize. The objective of our study is to design, develop and evaluate an open-source and user-friendly visualization interface for exploring the FHIR specification. We prototyped an interactive visualization tool for navigating and manipulating the FHIR core resources, profiles and extensions. The utility of the tool was evaluated using evaluation metrics mainly focusing on its interactive mechanism and content expressiveness. We demonstrated that the visualization techniques are helpful for navigating the HL7 FHIR specification and aiding its profiling.
{"title":"An Interactive Visualization Tool for HL7 FHIR Specification Browsing and Profiling.","authors":"Na Hong, Kui Wang, Sizhu Wu, Feichen Shen, Lixia Yao, Guoqian Jiang","doi":"10.1007/s41666-018-0043-8","DOIUrl":"10.1007/s41666-018-0043-8","url":null,"abstract":"<p><p>The rich semantic representation and sophisticated structure definition of the HL7 Fast Healthcare Interoperability Resources (FHIR) specification requires relatively great efforts to understand and utilize. The objective of our study is to design, develop and evaluate an open-source and user-friendly visualization interface for exploring the FHIR specification. We prototyped an interactive visualization tool for navigating and manipulating the FHIR core resources, profiles and extensions. The utility of the tool was evaluated using evaluation metrics mainly focusing on its interactive mechanism and content expressiveness. We demonstrated that the visualization techniques are helpful for navigating the HL7 FHIR specification and aiding its profiling.</p>","PeriodicalId":36444,"journal":{"name":"Journal of Healthcare Informatics Research","volume":null,"pages":null},"PeriodicalIF":5.9,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6784845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53224663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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