Jaber Rad

@inproceedings{nokey,

title = {Extracting Decision Paths via Surrogate Modeling for Explainability of Black Box Classifiers},

author = {Jaber Rad and Karthik Tennankore and Samina Abidi and Syed Sibte Raza Abidi},

doi = {10.1109/SDS60720.2024.00037},

year  = {2024},

date = {2024-03-30},

urldate = {2024-03-30},

booktitle = {2024 11th IEEE Swiss Conference on Data Science (SDS)},

pages = {213-220},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Extracting Surrogate Decision Trees from Black-box Models to Explain the Temporal Importance of Clinical Features in Predicting Kidney Graft Survival},

author = {Jaber Rad and Karthik Tennankore and Amanda Vinson and Syed Sibte Raza Abidi

},

year  = {2022},

date = {2022-06-14},

urldate = {2022-06-14},

booktitle = {20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax},

publisher = {Springer},

keywords = {Explainability, Kidney Disease, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rad2021,

title = {Using Interactive Visual Analytics to Optimize in Real-Time Blood Products Inventory at a Blood Bank},

author = {Jaber Rad and Jason Quinn and Calvino Cheng and Robert Liwski and Samina Raza Abidi and Syed Sibte Raza Abidi},

year  = {2021},

date = {2021-05-29},

urldate = {2021-05-29},

booktitle = {31st Medical Informatics Europe (MIE2021)},

keywords = {Blood Inventory Management, Clinical Decision Support Systems, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{Abidi2019,

title = {AI-Driven Pathology Laboratory Utilization Management via Data- and Knowledge-Based Analytics},

author = {Syed Sibte Raza Abidi and Jaber Rad and Ashraf Abusharekh and Patrice C. Roy and William Van Woensel and Samina Raza Abidi and Calvino Cheng and Bryan D. Crocker and Manal O. Elnenaei},

url = {https://link.springer.com/chapter/10.1007/978-3-030-21642-9_30},

doi = {10.1007/978-3-030-21642-9_30},

isbn = {978-3-030-21642-9},

year  = {2019},

date = {2019-06-26},

booktitle = {17th Conf. on Artificial Intelligence in Medicine (AIME2019), June 26-29},

pages = {241--251},

publisher = {Springer International Publishing},

address = {Poznan, Poland},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}