Proceedings. IEEE International Congress on Big Data最新文献

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SketchyCoreSVD: SketchySVD from Random Subsampling of the Data Matrix. SketchyCoreSVD:从数据矩阵的随机子采样的SketchySVD。

Proceedings. IEEE International Congress on Big Data Pub Date : 2019-12-01 Epub Date: 2020-02-24 DOI: 10.1109/bigdata47090.2019.9006345

Chandrajit Bajaj, Yi Wang, Tianming Wang

引用次数: 5

Experiences with the Twitter Health Surveillance (THS) System. Twitter健康监测(THS)系统的经验。

Proceedings. IEEE International Congress on Big Data Pub Date : 2017-06-01 Epub Date: 2017-09-11 DOI: 10.1109/BigDataCongress.2017.55

Manuel Rodríguez-Martínez

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引用次数: 0

Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records. 利用电子健康记录对舒适死亡结果进行预测建模。

Proceedings. IEEE International Congress on Big Data Pub Date : 2015-06-01 DOI: 10.1109/BigDataCongress.2015.67

Muhammad Kamran Lodhi, Rashid Ansari, Yingwei Yao, Gail M Keenan, Diana J Wilkie, Ashfaq A Khokhar

{"title":"Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.","authors":"Muhammad Kamran Lodhi, Rashid Ansari, Yingwei Yao, Gail M Keenan, Diana J Wilkie, Ashfaq A Khokhar","doi":"10.1109/BigDataCongress.2015.67","DOIUrl":"https://doi.org/10.1109/BigDataCongress.2015.67","url":null,"abstract":"Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this paper, we have used a nursing EHR system to build predictive models to determine what factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of-life (EOL) care.","PeriodicalId":91601,"journal":{"name":"Proceedings. IEEE International Congress on Big Data","volume":"2015 ","pages":"409-415"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BigDataCongress.2015.67","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34349962","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}

引用次数: 9