About Me
Research Interests
Machine Learning, Natural Language Processing, Data Visualization and Analytics, Big Data.
Publications
2020
Jaber Rad; Calvino Cheng; Jason G Quinn; Samina Abidi; Robert Liwski; Syed Sibte Raza Abidi
An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards Proceedings Article
In: International Conference on Artificial Intelligence in Medicine (AIME 2020), 2020.
Abstract | BibTeX | Tags: Big Data, Blood Inventory Management, Blood product wastage, Data visualization, Machine Learning, Sequence prediction, Visual analytics
@inproceedings{JRAD2020,
title = {An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products’ Discards},
author = {Jaber Rad and Calvino Cheng and Jason G Quinn and Samina Abidi and Robert Liwski and Syed Sibte Raza Abidi},
year = {2020},
date = {2020-08-01},
urldate = {2020-08-01},
booktitle = {International Conference on Artificial Intelligence in Medicine (AIME 2020)},
abstract = {Maintaining an equilibrium between shortage and wastage in blood inventories is challenging due to the perishable nature of blood products. Re-search in blood product inventory management has predominantly focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wast-age due to discards, related to the lifecycle of a blood product, is not well investigated. In this study, we investigate machine learning methods to analyze blood product transition sequences in a large real-life transactional dataset of Red Blood Cells (RBC) to predict potential blood product discard. Our prediction models are able to predict with 79% accuracy potential discards based on the blood product’s current transaction data. We applied advanced data visualizations methods to develop an interactive blood inventory dashboard to help laboratory managers to probe blood units’ lifecycles to identify discard causes.},
keywords = {Big Data, Blood Inventory Management, Blood product wastage, Data visualization, Machine Learning, Sequence prediction, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Maintaining an equilibrium between shortage and wastage in blood inventories is challenging due to the perishable nature of blood products. Re-search in blood product inventory management has predominantly focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wast-age due to discards, related to the lifecycle of a blood product, is not well investigated. In this study, we investigate machine learning methods to analyze blood product transition sequences in a large real-life transactional dataset of Red Blood Cells (RBC) to predict potential blood product discard. Our prediction models are able to predict with 79% accuracy potential discards based on the blood product’s current transaction data. We applied advanced data visualizations methods to develop an interactive blood inventory dashboard to help laboratory managers to probe blood units’ lifecycles to identify discard causes.
2016
Enayat Rajabi; Seyed-Mehdi-Reza Beheshti
Interlinking Big Data to Web of Data Book Chapter
In: vol. 18, pp. 133-145, Springer International Publishing, 2016, ISBN: 978-3-319-30263-8.
Abstract | Links | BibTeX | Tags: Big Data, Linked Data
@inbook{Rajabi2016,
title = {Interlinking Big Data to Web of Data},
author = {Enayat Rajabi and Seyed-Mehdi-Reza Beheshti},
doi = {10.1007/978-3-319-30265-2_6},
isbn = {978-3-319-30263-8},
year = {2016},
date = {2016-05-27},
volume = {18},
pages = {133-145},
publisher = {Springer International Publishing},
abstract = {The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.
},
keywords = {Big Data, Linked Data},
pubstate = {published},
tppubtype = {inbook}
}
The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.
2015
Sangwhan Cha; Ashraf Abusharekh; Syed Sibte Raza Abidi
Towards a 'Big' Health Data Analytics Platform Proceedings Article
In: First IEEE International Conference on Big Data Computing Service and Applications, BigDataService, Redwood City, CA, USA, pp. 233–241, IEEE Press, 2015, ISBN: 978-1-4799-8128-1.
Links | BibTeX | Tags: Big Data, Data Mining, Decision Support, Health Data Analytics
@inproceedings{DBLP:conf/bigdataservice/ChaAA15,
title = {Towards a 'Big' Health Data Analytics Platform},
author = {Sangwhan Cha and Ashraf Abusharekh and Syed Sibte Raza Abidi},
url = {http://dx.doi.org/10.1109/BigDataService.2015.13},
doi = {10.1109/BigDataService.2015.13},
isbn = {978-1-4799-8128-1},
year = {2015},
date = {2015-04-02},
booktitle = {First IEEE International Conference on Big Data Computing Service and Applications, BigDataService, Redwood City, CA, USA},
pages = {233--241},
publisher = {IEEE Press},
keywords = {Big Data, Data Mining, Decision Support, Health Data Analytics},
pubstate = {published},
tppubtype = {inproceedings}
}