Publications

@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}

}

@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}

}

@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}

}

@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}

}

@article{naqvi_jmir_21,

title = {Predicting Kidney Graft Survival using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study (under review)},

author = {Syed Asil Ali Naqvi and Karthik Tennankore and Amanda Vinson and Patrice C. Roy and Syed Sibte Raza Abidi},

journal = {Journal of Medical Internet Research},

keywords = {Machine Learning, Predictive Models},

pubstate = {published},

tppubtype = {article}

}