About Me
Research Interests
Your research interests.
Publications
2022
Jaber Rad; Karthik Tennankore; Amanda Vinson; Syed Sibte Raza Abidi
Extracting Surrogate Decision Trees from Black-box Models to Explain the Temporal Importance of Clinical Features in Predicting Kidney Graft Survival Proceedings Article
In: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022), June 14-17, 2022, Halifax, Springer, 2022.
BibTeX | Tags: Explainability, Kidney Disease, Machine Learning
@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}
}
William Van Woensel; Brett Taylor; Syed Sibte Raza Abidi
Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information Proceedings Article
In: Studies in Health Technology and Informatics, pp. 158–162, 2022, ISSN: 1879-8365.
Abstract | Links | BibTeX | Tags: Dictaphone, Machine Learning
@inproceedings{pmid35672991,
title = {Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information},
author = {William Van Woensel and Brett Taylor and Syed Sibte Raza Abidi},
doi = {10.3233/SHTI220052},
issn = {1879-8365},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {Studies in Health Technology and Informatics},
journal = {Stud Health Technol Inform},
volume = {290},
pages = {158--162},
abstract = {Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language. Our ultimate goal is to automate clinical transcription, particularly for Emergency Departments, with as an end-result a structured SOAP report. Towards this goal, we present the Adaptive Clinical Transcription System (ACTS). We compare the accuracy and processing times of state-of-the-art speech recognition tools, studying the feasibility of streaming-style dynamic transcription and opportunities of incremental learning.},
keywords = {Dictaphone, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
S. Majouni; JC. Kim; E. Sweeny; E. Keltie; Syed Sibte Raza Abidi
Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases Proceedings Article
In: International Conference on Medical Informatics in Europe (MIE2022), May 26-30, 2022, Nice, France, Springer, 2022.
BibTeX | Tags: Environmental Arsenic, Incident Cancer Cases, Machine Learning
@inproceedings{nokey,
title = {Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases},
author = {S. Majouni and JC. Kim and E. Sweeny and E. Keltie and Syed Sibte Raza Abidi},
year = {2022},
date = {2022-05-26},
urldate = {2022-05-26},
booktitle = {International Conference on Medical Informatics in Europe (MIE2022), May 26-30, 2022, Nice, France},
publisher = {Springer},
keywords = {Environmental Arsenic, Incident Cancer Cases, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Kathryn L. Young-Shand; Patrice C. Roy; Michael J. Dunbar; Syed S. R. Abidi; Janie L. Astephen Wilson
Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis Journal Article
In: Journal of Orthopaedic Research, 2022.
Abstract | Links | BibTeX | Tags: gait analysis, knee biomechanics, Machine Learning, phenotypes, Total knee arthroplasty
@article{https://doi.org/10.1002/jor.25363,
title = {Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis},
author = {Kathryn L. Young-Shand and Patrice C. Roy and Michael J. Dunbar and Syed S. R. Abidi and Janie L. Astephen Wilson},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jor.25363},
doi = {https://doi.org/10.1002/jor.25363},
year = {2022},
date = {2022-05-10},
urldate = {2022-05-10},
journal = {Journal of Orthopaedic Research},
abstract = {Abstract Knee osteoarthritis patient phenotyping is relevant to developing targeted treatments and assessing the treatment efficacy of total knee arthroplasty (TKA). This study aimed to identify clusters among TKA candidates based on demographic and knee mechanic features during gait, and compare gait changes between clusters postoperatively. TKA patients underwent 3D gait analysis 1-week pre (n = 134) and 1-year post-TKA (n = 105). Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms, extracting major patterns of variability. Age, sex, body mass index, gait speed, and frontal and sagittal pre-TKA angle and moment PC scores previously identified as relevant to TKA outcomes were standardized (mean = 0},
keywords = {gait analysis, knee biomechanics, Machine Learning, phenotypes, Total knee arthroplasty},
pubstate = {published},
tppubtype = {article}
}
2021
Syed Asil Ali Naqvi; Karthik Tennankore; Amanda Vinson; Syed Sibte Raza Abidi
Analyzing Association Rules for Graft Failure Following Deceased and Live Donor Kidney Transplantation Proceedings Article
In: 31st Medical Informatics Europe (MIE2021), 2021.
BibTeX | Tags: Association Rules, Machine Learning
@inproceedings{naqvi2021,
title = {Analyzing Association Rules for Graft Failure Following Deceased and Live Donor Kidney Transplantation},
author = {Syed Asil Ali Naqvi and Karthik Tennankore and Amanda Vinson and Syed Sibte Raza Abidi},
year = {2021},
date = {2021-05-29},
booktitle = {31st Medical Informatics Europe (MIE2021)},
keywords = {Association Rules, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
William Van Woensel; Patrice C Roy; Syed Sibte Raza Abidi; Samina Raza Abidi
Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods Journal Article
In: Artificial Intelligence in Medicine, vol. 108, pp. 101931, 2020, ISSN: 0933-3657.
Abstract | Links | BibTeX | Tags: Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care
@article{VANWOENSEL2020101931,
title = {Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods},
author = {William Van Woensel and Patrice C Roy and Syed Sibte Raza Abidi and Samina Raza Abidi},
url = {http://www.sciencedirect.com/science/article/pii/S0933365720301275
https://authors.elsevier.com/a/1bTwR3KEGaD3xR},
doi = {https://doi.org/10.1016/j.artmed.2020.101931},
issn = {0933-3657},
year = {2020},
date = {2020-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {108},
pages = {101931},
abstract = {In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.},
keywords = {Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care},
pubstate = {published},
tppubtype = {article}
}
0000
KL. Young-Shand; Patrice C. Roy; MJ. Dunbar; Syed Sibte Raza Abidi; JLA. Wilson
Assessing Knee Biomechanical Osteoarthritis Severity and Biomechanical Changes after Total Knee Arthroplasty using Self-Organizing Networks (under review) Journal Article
In: Journal of Biomechanics, 0000.
BibTeX | Tags: Clustering, Machine Learning, Self-Organizing Networks
@article{ys_jbm_21,
title = {Assessing Knee Biomechanical Osteoarthritis Severity and Biomechanical Changes after Total Knee Arthroplasty using Self-Organizing Networks (under review)},
author = {KL. Young-Shand and Patrice C. Roy and MJ. Dunbar and Syed Sibte Raza Abidi and JLA. Wilson},
journal = {Journal of Biomechanics},
keywords = {Clustering, Machine Learning, Self-Organizing Networks},
pubstate = {published},
tppubtype = {article}
}
KL. Young-Shand; Patrice C. Roy; MJ. Dunbar; Syed Sibte Raza Abidi; JLA. Wilson
Demographic and Gait Phenotypes among Total Knee Arthroplasty Candidates by Machine Learning Cluster Analysis Impacts Gait Improvement after Surgery (under review) Journal Article
In: Journal of Orthopedic Research , 0000.
BibTeX | Tags: Clustering, Machine Learning
@article{ys_jor_21,
title = {Demographic and Gait Phenotypes among Total Knee Arthroplasty Candidates by Machine Learning Cluster Analysis Impacts Gait Improvement after Surgery (under review)},
author = {KL. Young-Shand and Patrice C. Roy and MJ. Dunbar and Syed Sibte Raza Abidi and JLA. Wilson},
journal = {Journal of Orthopedic Research },
keywords = {Clustering, Machine Learning},
pubstate = {published},
tppubtype = {article}
}