- About Me
- Publications
- Supervision
- Brief Bio
About Me
I am a professor at the Faculty of Computer Science, Dalhousie University, Canada. I am also the Director of the health informatics graduate program.
I lead the NICHE (kNowledge Intensive Computing for Healthcare Enterprises) research group. My research interest span across three areas: (1) Health Informatics, (2) Knowledge Management, and (3) Health Data Analytics.
My research projects have been funded by both government agencies, organizations and industry, such as CANARIE, National Sciences and Engineering Research Council of Canada (NSERC), Canadian Foundation of Innovation (CFI), Canadian Institute for Health Research (CIHR), Nova Scotia Health Research Foundation (NSHRF), Green Shield Foundation Canada and Agfa Healthcare Canada.I teach specialized topics in health informatics such as healthcare knowledge management and healthcare data mining.
Publications
2019
Kathryn Young-Shand; Patrice C. Roy; Syed Sibte Raza Abidi; Michael Dunbar; Janie Astephen Wilson
Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome Proceedings Article
In: 2nd International Combined Meeting of Orthopaedic Research Societies, June 19-22, 2019, Montreal, Canada, 2019.
Abstract | BibTeX | Tags: Clustering, Total knee arthroplasty
@inproceedings{Young-Shand2019b,
title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},
author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},
year = {2019},
date = {2019-06-19},
booktitle = {2nd International Combined Meeting of Orthopaedic Research Societies, June 19-22, 2019, Montreal, Canada},
abstract = {Purpose:
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.},
keywords = {Clustering, Total knee arthroplasty},
pubstate = {published},
tppubtype = {inproceedings}
}
Purpose:
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.
Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.
Method:
Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.
Results:
Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.
Conclusion:
TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.
Kathryn Young-Shand; Patrice C. Roy; Syed Sibte Raza Abidi; Michael Dunbar; Janie Astephen Wilson
Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome Proceedings Article
In: Orthopaedic Research Society Conference (ORS), February 2-5, 2019 Austin, Texas, USA, 2019, (Recipient of the American Academy of Orthopaedic Surgeons (AAOS) Women’s Health Advisory Board (WHAB) Award).
BibTeX | Tags: Clustering, Total knee arthroplasty
@inproceedings{Young-Shand2019,
title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},
author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},
year = {2019},
date = {2019-02-02},
booktitle = {Orthopaedic Research Society Conference (ORS), February 2-5, 2019 Austin, Texas, USA},
note = {Recipient of the American Academy of Orthopaedic Surgeons (AAOS) Women’s Health Advisory Board (WHAB) Award},
keywords = {Clustering, Total knee arthroplasty},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Syed S.R. Abidi; Patrice C. Roy; Muhammad S. Shah; Jin Yu; Sanjun Yan
A data mining framework for glaucoma decision support based on optic nerve image analysis using machine learning methods Journal Article
In: Journal of Healthcare Informatics Research, vol. 2, no. 4, pp. 370-401, 2018.
Links | BibTeX | Tags: Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve
@article{Abidi2018,
title = {A data mining framework for glaucoma decision support based on optic nerve image analysis using machine learning methods},
author = {Syed S.R. Abidi and Patrice C. Roy and Muhammad S. Shah and Jin Yu and Sanjun Yan},
doi = {10.1007/s41666-018-0028-7},
year = {2018},
date = {2018-12-01},
journal = {Journal of Healthcare Informatics Research},
volume = {2},
number = {4},
pages = {370-401},
keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve},
pubstate = {published},
tppubtype = {article}
}
2007
Syed Sibte Raza Abidi; Paul Habib Artes; Sanjan Yun; Jin Yu
Automated Interpretation of Optic Nerve Images: A Data Mining Framework for Glaucoma Diagnostic Support. Proceedings Article
In: Stud Health Technol Inform, pp. 1309-13, Netherlands, 2007, ISSN: 0926-9630.
Abstract | BibTeX | Tags: Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve
@inproceedings{Abidi:StudHealthTechnolInform:2007,
title = {Automated Interpretation of Optic Nerve Images: A Data Mining Framework for Glaucoma Diagnostic Support.},
author = {Syed Sibte Raza Abidi and Paul Habib Artes and Sanjan Yun and Jin Yu},
issn = {0926-9630},
year = {2007},
date = {2007-01-01},
booktitle = {Stud Health Technol Inform},
volume = {129},
number = {Pt 2},
pages = {1309-13},
address = {Netherlands},
abstract = {Confocal Scanning Laser Tomography (CSLT) techniques capture high-quality images of the optic disc (the retinal region where the optic nerve exits the eye) that are used in the diagnosis and monitoring of glaucoma. We present a hybrid framework, combining image processing and data mining methods, to support the interpretation of CSLT optic nerve images. Our framework features (a) Zernike moment methods to derive shape information from optic disc images; (b) classification of optic disc images, based on shape information, to distinguish between healthy and glaucomatous optic discs. We apply Multi Layer Perceptrons, Support Vector Machines and Bayesian Networks for feature sub-set selection and image classification; and (c) clustering of optic disc images, based on shape information, using Self-Organizing Maps to visualize sub-types of glaucomatous optic disc damage. Our framework offers an automated and objective analysis of optic nerve images that can potentially support both diagnosis and monitoring of glaucoma.},
keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve},
pubstate = {published},
tppubtype = {inproceedings}
}
Confocal Scanning Laser Tomography (CSLT) techniques capture high-quality images of the optic disc (the retinal region where the optic nerve exits the eye) that are used in the diagnosis and monitoring of glaucoma. We present a hybrid framework, combining image processing and data mining methods, to support the interpretation of CSLT optic nerve images. Our framework features (a) Zernike moment methods to derive shape information from optic disc images; (b) classification of optic disc images, based on shape information, to distinguish between healthy and glaucomatous optic discs. We apply Multi Layer Perceptrons, Support Vector Machines and Bayesian Networks for feature sub-set selection and image classification; and (c) clustering of optic disc images, based on shape information, using Self-Organizing Maps to visualize sub-types of glaucomatous optic disc damage. Our framework offers an automated and objective analysis of optic nerve images that can potentially support both diagnosis and monitoring of glaucoma.
2005
Jin Yu; Syed Sibte Raza Abidi; Paul Habib Artes; Andrew R. McIntyre; Malcolm I. Heywood
Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma Proceedings Article
In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 23-24 June 2005, Dublin, Ireland, pp. 97–102, 2005.
Links | BibTeX | Tags: Classification, Clustering, Data Mining, Decision Support Systems, Glaucoma, Health Data Analytics, Optic Nerve
@inproceedings{DBLP:conf/cbms/YuAAMH05,
title = {Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma},
author = {Jin Yu and Syed Sibte Raza Abidi and Paul Habib Artes and Andrew R. McIntyre and Malcolm I. Heywood},
url = {http://dx.doi.org/10.1109/CBMS.2005.36},
year = {2005},
date = {2005-01-01},
booktitle = {18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 23-24 June 2005, Dublin, Ireland},
pages = {97--102},
crossref = {DBLP:conf/cbms/2005},
keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Glaucoma, Health Data Analytics, Optic Nerve},
pubstate = {published},
tppubtype = {inproceedings}
}
2001
Syed Sibte Raza Abidi; Kok Meng Hoe; Alwyn Goh
Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules Proceedings Article
In: Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001, Proceedings, pp. 248–257, 2001.
Links | BibTeX | Tags: Clustering, Data Mining, Health Data Analytics
@inproceedings{DBLP:conf/ida/AbidiHG01,
title = {Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules},
author = {Syed Sibte Raza Abidi and Kok Meng Hoe and Alwyn Goh},
url = {http://dx.doi.org/10.1007/3-540-44816-0_25},
year = {2001},
date = {2001-01-01},
booktitle = {Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001, Proceedings},
pages = {248--257},
crossref = {DBLP:conf/ida/2001},
keywords = {Clustering, Data Mining, Health Data Analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
1999
Jason Ong; Syed Sibte Raza Abidi
Data Mining Using Self-Organizing Kohonen Maps: A Technique for Effective Data Clustering & Visualization Proceedings Article
In: Proceedings of the International Conference on Artificial Intelligence, IC-AI '99, June 28 - July 1, 1999, Las Vegas, Nevada, USA, Volume 1, pp. 261–264, 1999.
BibTeX | Tags: Clustering, Data Mining
@inproceedings{DBLP:conf/icai/OngA99,
title = {Data Mining Using Self-Organizing Kohonen Maps: A Technique for Effective Data Clustering & Visualization},
author = {Jason Ong and Syed Sibte Raza Abidi},
year = {1999},
date = {1999-01-01},
booktitle = {Proceedings of the International Conference on Artificial Intelligence, IC-AI '99, June 28 - July 1, 1999, Las Vegas, Nevada, USA, Volume 1},
pages = {261--264},
crossref = {DBLP:conf/icai/1999-1},
keywords = {Clustering, Data Mining},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
Supervision
Current Post Doctoral Fellows
- William Van Woensel. Investigations in mobile Reasoning Frameworks.
- Patrice Roy. Semantic Web based Event Processing for Smart Environments.
Past Post Doctoral Fellows
- Borna Jafarpour. Semantic Web for Clinical Decision Support, Feb. 2014 – Dec. 2014.
- Sangwhan Cha. Big Data Analytics Framework for Health, Jan. 2014 – Nov. 2014.
- Newras Al-Haider. Semantic Integration of Heterogeneous Data, April 2013 – Dec. 2014.
- Greg Lee. Computerizing Behaviour Modelling to Develop Personalized Interventions, June 2013 – Feb. 2014. (Co-Supervision)
- Ashraf Abusharekh. Big Data Management for an E-Science Framework, July 2009 – Dec. 2013.
- Jocelyne Fadoul. Investigations in Clinical Pathways, Sept. 2011 – Aug. 2012
- Andrew McIntyre. Machine Learning Methods for Analyzing Optic Disc Data for Glaucoma Detection, Jan. 2008 – Dec. 2008.
Current PhD (Computer Science and IDPhD)
- Hossein Hassanzadeh. TBD.
- Calvino Cheng. Process Mining to Abstract Clinical Pathways from Laboratory Information Data (IDPhD).
- Ghdeer Tashkandi. Personalized Patient Education Programs Driven by Computerized Health Models (IDPhD).
- Krista Elvidge. A Lifelong Patient Educational Intervention Program: Investigating the Role of Behavioural Models (IDPhD).
- Muzammil Bashir Ahmed. A Semantic Web Framework for Personalized Patient Care Planning in Hospital Settings (IDPhD).
Completed Ph.D. Thesis
- Borna Jafarpour. Ontology Merging Using Semantically-Defined Merge Criteria And Owl Reasoning Services: Towards Execution-Time Merging Of Multiple Clinical Workflows, Dec. 2013.
- Samuel Stewart. Combining Social Network And Semantic Content Analysis To Improve Knowledge Translation In Online Communities Of Practice (IDPhD), Dec. 2013.
- Joyline Makani. Knowledge Management in Knowledge Intensive Organizations: An Investigation of Factors Influencing Choices of Knowledge Management Systems (IDPhD), May 2012 (Co-Supervisor).
- Syed Sajjad Hussain. K-MORPH: Knowledge Morphing via Reconciliation of Contextualized Sub-ontologies, March 2011.
- Cheah Yu-N. A Knowledge Management Info-structure for Knowledge Creation: A Scenario-based Strategy for the Explication and Crystallisation of Tacit Knowledge, Universiti Sains Malaysia, May 2002.
Current Masters Theses (Computer Science & Health Informatics)
- Arun Salunke. Topic in Health Data Mining.
- Ahmad Marwan Ahmad. Topic in Knowledge Translation.
- Wasif Baig. Modeling Behaviour Modification Strategies to Derive Patient Educational Interventions, (Co-Supervisor).
Completed Masters Thesis
- Swapnil Mahajan. A Generic Framework for Providing Psychosocial Support to Patients through an Online Virtual World, Jan. 2015 (Co-Supervisor).
- Hani Mufti. Predictors of Post-operative Neurocognitive Complications in Patients Undergoing Cardiac Operation: A Predictive Analytics Approach, August 2014.
- Ehsan Maghsoudlou. Integrating Protocol-Driven Decision Support Within E-Referral System: Supporting Primary Care Practitioners For Spinal Care Consultation And Triaging, April 2014.
- Nelofar Kureshi. Personalized Medicine: Development of a Predictive Computational Model for Personalized Therapeutic Interventions, August 2013 (Co-Supervisor).
- Ali Haider Zaidi. An OWL Ontology for Modeling HL-7 Compliant Electronic Patient Records for Chronic Disease Management, Dec. 2012.
- Shirin Sharif. Characterization of a Longitudinal Care Plan Model For Managing Chronic Diseases: A Care Plan Ontology to Computerize Paper-Based Care Plans, April 2012.
- Nima Hashemian. Modelling Clinical Pathways as Business Process Models Using Business Process Modelling Notation, March 2012.
- Mostafa Omaish. Ontology-based Knowledge Model for an ACS Management Clinical Guideline: Handling Updates and Institutional Priorities, Dec. 2011.
- Syed Farrukh Mehdi. Exploiting Semantics and Syntax for service Specification and Signature Matching: The S5 Web Service Matchmaker, Dec. 2011.
- Ashraf Mohammed Iqbal. An Ontology-Based Electronic Medical Record for Chronic Disease Management, Feb. 2011 (Co-Supervisor).
- Ryan Druggan. Clinical Knowledge Modelling to Develop E-Clinics for Neuropituary Care, April 2010.
- Brett Taylor. Re-Imaging Surveillance in the Context of Emergency Care, August 2009.
- Muzammil Bashir Ahmad. Ontology Modelling for Nursing Care Plans and Clinical Practice Guidelines, April 2009.
- Shapoor Sheghani. Knowledge Modeling to Develop a Clinical Practice Guideline Ontology: Towards Computerization and Merging of Clinical Practice Guidelines, Sept. 2007.
- Katrina Hurley. Practice-Oriented Knowledge Abstraction in Development of an Ontology to Model Clinical Pathways, May 2007.
- Philip O’Brien. Semantic Web Approach for Context-Aware Knowledge Sharing, March 2007.
- Selana Davis. Personalization of Cardiovascular Risk Management Using Linkages of SCORE and Behaviour Change Readiness to Web-based Education, August 2006.
- Aliko Mwakatobe. Information Personalization on the Semantic Web Using Reasoning, August 2006.
- Yan Zeng. Pursuing Information Personalization as a Constraint Satisfaction Problem, Dec. 2005.
- Zeina Chedrawy. PRECISE: A Hybrid of Item-Based Collaborative Filtering and Case Based Reasoning for Contextual Information Personalization. July 2005.
- Sanjun Yan. Using Unsupervised Learning Methods to Sub-classify Optic Disk Damage Images, April 2005.
- Xuan Hu. Evaluating Discretization Techniques to Discretize Visual Field Data, April 2005.
- Qingshuang Jiang. Discovering Generalized Symbolic Rule from Un-annotated Data: A Hybrid of Rough Sets and Attribute Oriented Induction, April 2005.
- Jin Yu. A Hybrid Feature Selection Method Classifying Optic Nerve Images, April 2005.
- Brent Jones. Leveraging Clinical Practice Guidelines to Develop Objective User Models for Customizing Healthcare Information to Manage High Cholesterol, Sept. 2004.
- Zahid Hasan Zaidi. A Multi-Agent Info-Structure to Provide End-User Data Mining: From Automatic Query Generation to Algorithm Selection to Result Visualization. Universiti Sains Malaysia, August 2004.
- Zafar Iqbal Hashmi. An Intelligent Agent Framework for Context-Sensitive Knowledge Retrieval from an Enterprise Memory. Universiti Sains Malaysia, August 2004.
- Chong Yong Han. Adaptive Hypermedia and Constraint Satisfaction Programming: Towards Automated Generation of Personalized and Factually Consistent Healthcare Information. Universiti Sains Malaysia, Oct. 2003.
- Selvakumar Manickam. Automated Transformation of Generic Electronic-Medical-Records To Specialised Cases: Towards an Internet-Mediated, Multi-Tier Case-Base-Reasoning Info-Structure. Universiti Sains Malaysia, December 2002.
- Hoe Kok Meng. Knowledge Discovery from Unannotated Datasets: A Symbolic Rule Extraction Framework Featuring Neural Network Analysis and Rough Set Decision Synthesis. Universiti Sains Malaysia, July 2002.
- Wai Han Soo. A CBR Framework for Personalizing Healthcare Information. Universiti Sains Malaysia, August 2002.
- Jason Ong Hing Soon. Exploratory Data Mining Using Self-Organising Maps: An Info-Structure for Data Clustering. Universiti Sains Malaysia, 2000.
Current Masters Projects
- Perez Whitfield. Development of a Web-based Provincial NOAC Authorization System.
Completed Masters Projects
- Hari Gudigundla. Semantic Web Query Engine For Ocean Data, June 2014.
- Jagjit Sandhu. Ocean data browser: An Online Tool to Cluster and Analyze Multidimensional Ocean Data, Dec. 2011.
- Xin Sheng. Developing Social Networks from On-Line Discussions Between Emergency Care Health Professionals, Dec. 2004.
- Winston Ying. A Generic Framework to Incorporate Clinical Practice Guidelines for Information Personalization, Oct. 2004.
- Muqueem Lodhi. A Web Services Framework for Document Retrieval, Oct. 2004
- Fuhai Li. A Distributed Clinical Information System for Managing Breast Cancer Surgery, July 2004.
- Zhenming Hu. A Case-Based Reasoning Framework for Information Personalization, March 2004.
- Zhilun Gao. Applying Data Mining and Statistical Techniques to Create a Frailty Index from Geriatrics Data, Dec. 2003.
- Zarmina Nadeem. Using Decision Trees to Develop Predictive Models for Improved Product Sales Campaigns, Sept. 2003.
- Teh Kia Hock. Data-Mining for Customer Relationship Management (CRM) in an E-Commerce Environment, Universiti Sains Malaysia, May 2001.
- Lecthumanwan Balasubramaniam. A Generic Web-Mining Framework for Analysing Web Usage Patterns at Web-Portals, Universiti Sains Malaysia, May 2001.
- Chuah JuJu. Prediction and Sensitivity Analysis of General Data Using Neural Networks, Universiti Sains Malaysia, February 2000.
- Teh Woan Yong. Trend Analysis and Forecasting of Computer Network Traffic Using Neural Networks, Universiti Sains Malaysia, May 2000.
- Thiang Bee Nee. Using Neural Networks for DNA Sequence Analysis: Splice Site Prediction, Universiti Sains Malaysia, May 2000.
- Ng Sim Yam. Anatomy Advisor on the World Wide Web, Universiti Sains Malaysia, September 1999.
- Lee Gim Hoo. A System for Electronic Health Survey over the World Wide Web, Universiti Sains Malaysia, Sept. 1999.
Brief Bio
Academic Qualifications
- Ph.D. University of Surrey, Guildford, UK: 1994
Specialization: Computer Sciences (Neural Networks & AI) - M.S. University of Miami, Miami, USA: 1989
Specialization: Computer Engineering - B.Engg. N.E.D. University of Engineering & Technology, Karachi, Pakistan: 1986
Specialization: Electronic Engineering
Academic Honours
- Best paper award at 16th International Symposium on Health Information Management Research (iSHIMR), Halifax, June 2013.
- Best paper award at 2nd Advances in Health Informatics Conference, Toronto, April 2012.
- Research Excellence Award, Faculty of Computer Science, Dalhousie University, 2011.
- Best paper award at 11th International Symposium on Health Information Management Research (iSHIMR), Halifax, July 2006
- Best paper award at 39th IEEE Hawaii International Conference on System Sciences (IT in healthcare track), Hawaii, January 2006.
- Best paper award at 38th IEEE Hawaii International Conference on System Sciences (IT in healthcare track), Hawaii, January 2005.
- Best paper award at 16th International Congress of the European Federation for Medical Informatics (MIE’2000), Hannover, 2000.
- VHK International Award for Innovation in Medical Informatics, Hannover, August 2000. The award was adjudged based on my R&D work presented at the International Congress of Medical Informatics in Europe (MIE’2000) in Hannover, Germany.
Employment Record
- Professor (July 2007 – To date), Faculty of Computer Science, Dalhousie University, Halifax, Canada
- Professor (April 2013 – To date) [Cross-Appointment], Department of Medicine, Dalhousie University, Halifax, Canada.
- Director of Health Informatics (July 2005 – To date), Faculty of Computer Science, Dalhousie University, Halifax, Canada.
- Adjunct Professor (March 2008 – 2012), Multimedia University, Kuala Lumpur, Malaysia.
- Associate Professor (Jan 2002 – June 2007), Faculty of Computer Science, Dalhousie University, Halifax, Canada.
- Associate Professor (May 1999 – Dec 2001), School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia.
- Lecturer (Oct. 1995 – April 1999), School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia.
- Research Officer (March 1993 – Dec 1994), AI Group, Dept. of Computing Sciences, Univ. of Surrey, Guildford, England.