Syed Sibte Raza Abidi

@article{JAFARPOUR2019,

title = {Execution-Time Integration of Clinical Practice Guidelines To Provide Decision Support for Comorbid Conditions},

author = {Borna Jafarpour and Samina Raza Abidi and William Van Woensel and Syed Sibte Raza Abidi},

url = {https://authors.elsevier.com/a/1Yf0D3KEGa1e9B},

doi = {10.1016/j.artmed.2019.02.003},

issn = {0933-3657},

year  = {2019},

date = {2019-01-01},

journal = {Artificial Intelligence in Medicine},

volume = {94},

pages = {117-137},

abstract = {Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/14993,

title = {Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study},

author = {Hani Nabeel Mufti and Gregory Marshal Hirsch and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {http://www.ncbi.nlm.nih.gov/pubmed/31558433},

doi = {10.2196/14993},

issn = {2291-9694},

year  = {2019},

date = {2019-01-01},

journal = {JMIR Med Inform},

volume = {7},

number = {4},

pages = {e14993},

abstract = {Background: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective: This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods: We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), na"ive Bayesian, random forest, and decision trees. Results: Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm's performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions: Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model's performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients' outcomes.},

keywords = {delirium; cardiac surgery; machine learning; predictive modeling},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{SWJ-WVW-2018,

title = {Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL},

author = {William Van Woensel and Syed Sibte Raza Abidi},

url = {http://www.semantic-web-journal.net/system/files/swj1881.pdf},

year  = {2018},

date = {2018-08-06},

journal = {Semantic Web Journal},

abstract = {Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.},

keywords = {Mobile Computing, OWL2 RL, Semantic Web reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/medinform.9629c,

title = {Diabetes-Related Behavior Change Knowledge Transfer to Primary Care Practitioners and Patients: Implementation and Evaluation of a Digital Health Platform},

author = {Samina Abidi and Michael Vallis and Helena Piccinini-Vallis and Ali Syed Imran and Raza Syed Sibte Abidi},

url = {http://medinform.jmir.org/2018/2/e25/},

doi = {10.2196/medinform.9629},

year  = {2018},

date = {2018-04-18},

journal = {JMIR Med Inform},

volume = {6},

number = {2},

pages = {e25},

abstract = {Background: Behavioral science is now being integrated into diabetes self-management interventions. However, the challenge that presents itself is how to translate these knowledge resources during care so that primary care practitioners can use them to offer evidence-informed behavior change support and diabetes management recommendations to patients with diabetes. Objective: The aim of this study was to develop and evaluate a computerized decision support platform called ``Diabetes Web-Centric Information and Support Environment'' (DWISE) that assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines--based recommendations to an individual patient and empower the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. Methods: A health care knowledge management approach is used to implement DWISE so that it features the following functionalities: (1) assessment of primary care practitioners' readiness to administer validated behavior change interventions to patients with diabetes; (2) educational support for primary care practitioners to help them offer behavior change interventions to patients; (3) access to evidence-based material, such as the Canadian Diabetes Association's (CDA) clinical practice guidelines, to primary care practitioners; (4) development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (5) educational support for patients to help them achieve behavior change; and (6) monitoring of the patients' progress to assess their adherence to the behavior change program and motivating them to ensure compliance with their program. DWISE offers these functionalities through an interactive Web-based interface to primary care practitioners, whereas the patient's self-management program and associated behavior interventions are delivered through a mobile patient diary via mobile phones and tablets. DWISE has been tested for its usability, functionality, usefulness, and acceptance through a series of qualitative studies. Results: For the primary care practitioner tool, most usability problems were associated with the navigation of the tool and the presentation, formatting, understandability, and suitability of the content. For the patient tool, most issues were related to the tool's screen layout, design features, understandability of the content, clarity of the labels used, and navigation across the tool. Facilitators and barriers to DWISE use in a shared decision-making environment have also been identified. Conclusions: This work has provided a unique electronic health solution to translate complex health care knowledge in terms of easy-to-use, evidence-informed, point-of-care decision aids for primary care practitioners. Patients' feedback is now being used to make necessary modification to DWISE.},

keywords = {clinical decision support system},

pubstate = {published},

tppubtype = {article}

}

@article{COX2018149c,

title = {Optimizing Primary Care Management of Atrial Fibrillation: The Rationale and Methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) Study},

author = {Jafna L Cox and Ratika Parkash and Syed SR Abidi and Lehana Thabane and Feng Xie and James MacKillop and Samina R Abidi and Antonio Ciaccia and Shurjeel H Choudhri and A Abusharekh and Joanna Nemis-White},

url = {http://www.sciencedirect.com/science/article/pii/S0002870318301170},

doi = {10.1016/j.ahj.2018.04.008},

issn = {0002-8703},

year  = {2018},

date = {2018-01-01},

volume = {201},

pages = {149 - 157},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleb,

title = {Preoperative Education System to Assist Patients Undergoing TAVI Surgery: A Digital Health Solution},

author = {Ali Naserian Mojadam and Najaf Nadeem and Hussein Beydoun and Samina Abidi and Ali Rizvi and Syed Abidi},

doi = {10.4172/2157-7420.1000313},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Health & Medical Informatics},

volume = {09},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Maghsoud-Lou2017,

title = {Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics},

author = {Ehsan Maghsoud-Lou and Sean Christie and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {https://doi.org/10.1007/s10916-017-0791-7},

doi = {10.1007/s10916-017-0791-7},

issn = {1573-689X},

year  = {2017},

date = {2017-08-01},

journal = {Journal of Medical Systems},

volume = {41},

number = {9},

pages = {139},

abstract = {Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Roy2017b,

title = {Possibilistic Activity Recognition with Uncertain Observations to Support Medication Adherence in an Assisted Ambient Living Setting},

author = {Patrice C. Roy and Samina R. Abidi and Syed S.R. Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S0950705117303246},

doi = {10.1016/j.knosys.2017.07.008},

issn = {09507051},

year  = {2017},

date = {2017-07-06},

journal = {Knowledge-Based Systems},

volume = {133},

pages = {156-173},

abstract = {A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources' types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.},

keywords = {Activity Recognition, Ambient Intelligence, medication adherence},

pubstate = {published},

tppubtype = {article}

}

@article{Stewart2017,

title = {Leveraging Medical Taxonomies to Improve Knowledge Management within Online Communities of Practice: The Knowledge Maps System},

author = {Samuel Alan Stewart and Syed Sibte Raza Abidi},

year  = {2017},

date = {2017-04-26},

journal = {Journal of Computer Methods and Programs in Biomedicine},

volume = {143},

pages = {121-127},

keywords = {Knowledge Management, Online Practice Communities},

pubstate = {published},

tppubtype = {article}

}

@article{Mohammadhassanzadeh2017,

title = {Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support},

author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {http://rdcu.be/paPY},

doi = {10.1186/s13040-017-0123-y},

year  = {2017},

date = {2017-02-10},

journal = {Journal of BioData Mining},

volume = {10},

number = {7},

abstract = {Background

Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.



Results

We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.



Conclusions

We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.},

keywords = {Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Subbu16,

title = {A Semantic Web Framework For Representing, Linking And Analyzing Medical Data For Optimizing Laboratory Utilization},

author = {Suria Kala Subbu},

url = {https://dalspace.library.dal.ca/xmlui/handle/10222/72131?show=full},

year  = {2016},

date = {2016-09-01},

abstract = {In this thesis, we investigate semantic web based methods for representing, linking and analyzing medical data. The main challenge addressed in this work is the transformation of data stored in a relational database to an ontological model that allows to represent as RDF triples and to link the data with external data sources using linked data principles. We have implemented a semantic analytics framework that comprises the following elements: (a) Domain-specific ontology to represent the data model and data inference. (b) RDMS data extraction using a domain-specific ontology (TBOX) based on the relational database schema; (c) Ontology instantiation (ABOX) that involves converting the relational data in terms of RDF triples. A key feature of our approach is the data is not physically migrated from the RDBS to RDF, rather we dynamically materialize the RDF triples thus avoiding the creation of a large RDF dump; (d) Linking the RDF data with available open data in RDF format using ontology-based concept alignments; and (e) Semantic analytics using SPARQL to identify semantically-salient patterns within the data. We have applied our semantic analytics data to analyze pathology lab data (over 5 years of pathology order data), where we were able to identify prevalent order-sets inherent within the data, and we also evaluated the change in the frequent order-sets over a five year time period.},

keywords = {Health Data Analytics, Health Informatics, Knowledge Based Systems, Knowledge Linkage, Knowledge Management, Ontology Engineering, Semantic Web},

pubstate = {published},

tppubtype = {article}

}

@article{Kureshi:IeeeJBiomedHealthInform:2014,

title = {A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer.},

author = {Nelofar Kureshi and Syed Sibte Raza Abidi and Christian Blouin},

url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6974996&isnumber=7369993},

doi = {10.1109/JBHI.2014.2377517},

issn = {2168-2208},

year  = {2016},

date = {2016-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {20},

number = {1},

pages = {424-431},

abstract = {Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, EGFR mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR-TKIs. Overall, the highest classification accuracy was 76.56% and the AUC was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC},

keywords = {Cancer, Data Mining, Decision Trees, Health Data Analytics, Personalized Medicine, Prediction Model},

pubstate = {published},

tppubtype = {article}

}

@article{Jafarpour:IeeeJournalOfBiomedicalAndHealthInformat,

title = {Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines},

author = {Borna Jafarpour and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6990486&isnumber=7369993},

doi = {10.1109/JBHI.2014.2383840},

issn = {2168-2194},

year  = {2016},

date = {2016-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {20},

number = {1},

pages = {388-398},

abstract = {Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially Web Ontology Language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + Semantic Web Rule Language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.},

keywords = {Clinical Decision Support Systems, Clinical Practice Guidelines, OWL, Semantic Web},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/kais/WongAJ14,

title = {A Multi-Phase Correlation Search Framework for Mining Non-Taxonomic Relations from Unstructured Text},

author = {Mei Kuan Wong and Syed Sibte Raza Abidi and Ian D. Jonsen},

url = {http://dx.doi.org/10.1007/s10115-012-0593-7},

year  = {2014},

date = {2014-01-01},

journal = {Knowl. Inf. Syst.},

volume = {38},

number = {3},

pages = {641--667},

keywords = {Non-Taxonomic Relations, Ontology, Text Mining},

pubstate = {published},

tppubtype = {article}

}

@article{Abidi:CanJDiabetes:2014,

title = {D-WISE: Diabetes Web-Centric Information and Support Environment: conceptual specification and proposed evaluation.},

author = {Samina Abidi and Michael Vallis and Syed Sibte Raza Abidi and Helena Piccinini-Vallis and Syed Ali Imran},

issn = {2352-3840},

year  = {2014},

date = {2014-01-01},

journal = {Can J Diabetes},

volume = {38},

number = {3},

pages = {205-11},

address = {Canada},

abstract = {OBJECTIVE: To develop and evaluate Diabetes Web-Centric Information and Support Environment (D-WISE) that offers 1) a computerized decision-support system to assist physicians to A) use the Canadian Diabetes Association clinical practice guidelines (CDA CPGs) to recommend evidence-informed interventions; B) offer a computerized readiness assessment strategy to help physicians administer behaviour-change strategies to help patients adhere to disease self-management programs; and 2) a patient-specific diabetes self-management application, accessible through smart mobile devices, that offers behaviour-change interventions to engage patients in self-management. METHODS: The above-mentioned objectives were pursued through a knowledge management approach that involved 1) Translation of paper-based CDA CPGs and behaviour-change models as computerized decision-support tools that will assist physicians to offer evidence-informed and personalized diabetes management and behaviour-change strategies; 2) Engagement of patients in their diabetes care by generating a diabetes self-management program that takes into account their preferences, challenges and needs; 3) Empowering patients to self-manage their condition by providing them with personalized educational and motivational messages through a mobile self-management application. The theoretical foundation of our research is grounded in behaviour-change models and healthcare knowledge management. We used 1) knowledge modelling to computerize the paper-based CDA CPGs and behaviour-change models, in particular, the behaviour-change strategy elements of A) readiness-to-change assessments; B) motivation-enhancement interventions categorized along the lines of patients' being ready, ambivalent or not ready; and C) self-efficacy enhancement. The CDA CPGs and the behaviour-change models are modelled and computerized in terms of A) a diabetes management ontology that serves as the knowledge resource for all the services offered by D-WISE; B) decision support services that use logic-based reasoning algorithms to utilize the knowledge encoded within the diabetes management ontology to assist physicians by recommending patient-specific diabetes-management interventions and behaviour-change strategies; C) a mobile diabetes self-management application to engage and educate diabetes patients to self-manage their condition in a home-based setting while working in concert with their family physicians. RESULTS: We have been successful in creating and conducting a usability assessment of the physician decision support tool. These results will be published once the patient self- management application has been evaluated. CONCLUSIONS: D-WISE will be evaluated through pilot studies measuring 1) the usability of the e-Health interventions; and 2) the impact of the interventions on patients' behaviour changes and diabetes control},

keywords = {Behaviour Modelling, Behavioural Theory, Diabetes Management, Health Informatics, Mobile Health, Patient Education},

pubstate = {published},

tppubtype = {article}

}