The objective of this thesis is to formalize, model, align and operationalize the evidence-based clinical algorithms of co-morbid chronic heart failure (CHF) and atrial fibrillation (AF) in order to provide clinical recommendations, care coordination and decision support to general practitioners (GPs). This thesis addresses following healthcare knowledge modeling issues: (a) modeling of healthcare knowledge, especially in terms of clinical guidelines and clinical pathways, to develop an ontology-based knowledge model for handling co-morbid diseases; (b) computerization of clinical pathways to offer point-of-care decision support; (c) alignment of ontologically-modeled disease-specific clinical pathways to handle co-morbid diseases; and (d) the provision of computerized decision support to GPs, based on modeled clinical guidelines and pathways, to assist them in handling co-morbid diseases. An elaborate OWL CP ontology for co-morbid CHF and AF was developed that can be executed to support the diagnosis and management of co-morbid CHF and AF in a general practice setting. / In this thesis we present an ontology based decision-support framework for handling co-morbidities by the alignment of ontologically modeled clinical practice guidelines (CPGs). The objective of this thesis is to formalize, model, align and operationalize the evidence-based clinical algorithms of co-morbid chronic heart failure (CHF) and atrial fibrillation (AF) in order to provide evidence-based clinical recommendations, care coordination and decision support to general practitioners (GPs) for effective management of CHF and AF. In this regard, the thesis addresses the following healthcare knowledge modeling issues: (a) modeling of healthcare knowledge, especially in terms of clinical guidelines and clinical pathways, to develop an ontology-based healthcare knowledge model for handling co-morbid diseases; (b) computerization of clinical pathways to offer point-of-care decision support; (c) alignment of ontologically-modeled disease-specific clinical pathways to handle co-morbid diseases; and (d) the provision of computerized decision support to general practitioners, based on modeled clinical guidelines and pathways, to assist them in handling chronic and co-morbid diseases. An elaborate OWL CP ontology for co-morbid CHF and AF—the CP ontology was developed that can be executed to support the diagnosis and management of co-morbid CHF and AF in a general practice setting. We have developed a decision support framework termed COMET (Co-morbidity Ontological Modeling & ExecuTion) that can handle three patient care scenarios, (i) patient has CHF; (ii) patient has AF; and (iii) patient develops a co-morbidity of both AF and CHF. COMET is accessible by web and is designed for GPs. COMET has been evaluated, both by simulated cases and by health professionals (GP and specialist), for its ability to handle single disease and comorbid care scenarios based on patient data and related constraints. The output at every phase is compared with the expected output as per single disease or comorbid management. Our results show that the resultant sequence of plans and their outcomes are comparable to the CP knowledge. Also, our ontology was able to handle any updates in the CP knowledge as advised by the domain experts

Constructing a Bayesian Network requires the conditional probabilities table (CPT) to be acquired, one for each variable or node in the network. When data mining is not available, CPTs must be acquired from the domain experts. The complexity of the direct elicitation is exponential on the number of parents of a variable, making direct elicitation from human experts impractical for a large number of causes. Causal models such as Noisy-OR, Noisy-AND, Noisy-MIN, Noisy-MAX and Recursive Noisy-OR have been developed that allow CPTs acquisition to be achieved with linear complexity on the number of causes. Their representation power is measured by their ability to encode the causal interactions. Causal interactions can be categorized into two types: reinforcing and undermining. The Non-Impeding Noisy-AND or NIN-AND tree causal model, developed by Xiang and Jia, is capable of modeling both types of interaction while retaining the linear complexity. The main challenge in utilizing the NIN-AND tree model to generate a CPT is that it requires its tree topology to be elicited. A NIN-AND tree topology is an encoding of the causal interactions between the causes. In this work we present two methods, Structure Elimination (SE) and Pairwise Causal Interaction (PCI), that allow indirect elicitations of the NIN-AND tree topology using some additional probabilities elicited from experts. We conduct human-based experiment to investigate the e ectiveness of the two methods in terms of accuracy by comparing them to the Direct Numerical (DN) elicitation method. We recruit participants from second year Computer Science students at the University of Guelph. The process involves training a participant into domain expert using a known NIN-AND tree model then acquire another NIN-AND tree model by applying the SE and PCI methods. The CPTs produced by the acquired NIN-AND tree models are then compared to the one obtained by using the DN method. Comparable CPT accuracies are obtained among models generated by di erent methods, even though SE and PCI requires a much smaller number of parameters in comparison to DN.

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Background: Considerable amount of inappropriate antibiotic utilization has been observed in both acute and non-acute care hospitals. Literature has shown that strategies such as an order entry (OE) and computer decision support system (DSS) have improved prescribing practices in acute care settings. However, there is limited research on the effect of OE on antibiotic utilization in non-acute care settings. Objective: To determine the relationship between OE with DSS and antibiotic utilization in a complex continuing care and rehabilitation hospital. Methods: A retrospective analysis of OE and Pharmacy dispensing data, prospectively collected between July 1, 1999 and December 31, 2005, was conducted. Dispensing events for oral and intravenous antibiotics were merged with corresponding OE’s (when present) to form a final database of 4,739 dispensing events with 2,397 OE’s. The presence of OE and the proportion of OE to dispensing events formed the exposure variable while antibiotic utilization in defined daily dose (DDD) was calculated using dose and number of doses of an antibiotic. Antibiotic utilization was examined at the hospital and individual service in-patient unit levels (complex continuing care/CCC and rehabilitation medicine/REH). Statistical analysis consisting of multiple regression modeling was conducted to determine the association between use of OE and antibiotic utilization. Results: A best-fit model using multiple regression analysis at hospital level indicated a significant positive relationship between the presence of OE and antibiotic utilization when service, patient age, gender and antibiotic classes were accounted for. This model explained 11% of the variation in antibiotic utilization. No significant associations were found in the CCC in-patient unit while in the REH in-patient unit a significant positive relationship between the presence of OE and antibiotic utilization was observed. Similarly, antibiotic utilization increased significantly with increase in the proportion of OE to dispensing events at the hospital and REH in-patient unit levels but not in the CCC in-patient unit. Conclusion: The results of this study demonstrate that antibiotic utilization increased over the years following the inception of the OE system with DSS at the study hospital. Further research is required to examine the effect of OE with a rudimentary DSS on antibiotic utilization management in non-acute care. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2007-07-13 10:47:38.035

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The 'Arco Seco' or 'Dry Arc' region of the Republic of Panama is considered to be the driest in the country, where many areas of this region experience severe water stress during the months of January through May. This study was conducted to develop a tool for the assessment of sustainable implementation of water harvesting through ponds for agricultural purposes in the region. A computer based Decision Support System (DSS) has been developed specifically for the Arco Seco region in order to facilitate pond storage capacity estimation. As part of the DSS, four computer programs have been designed for four different case scenarios; the first one is for sites that have high water demand and no topographical restrictions for pond size; the second is for fairly high water demand, no topographical restrictions for pond size, and for farmers who wish to have a backup of water to use mostly during drier years; the third is for low water demand, usage during the dry season only, and topographical restrictions for pond size, and finally the fourth is for constant water demand throughout the year, and for sites where runoff is the only water source.* / *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).

Lung cancer is the leading cause of cancer-related deaths among men and women. Non-Small Cell Lung Cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. In the past decade, discovery of Epidermal Growth Factor Receptor (EGFR) mutations have heralded a new paradigm of personalized treatment for NSCLC. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Despite these advances, 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. This thesis demonstrates the development of a predictive computational model for personalized therapeutic interventions in advanced NSCLC. The findings suggest that the combination of clinical and genetic data significantly improves the model’s predictive performance for tumor response than clinical data alone.

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This dissertation formulates and develops models and solution approaches for plug-in electric vehicle (PEV) charging station installation. The models are formulated in the form of bilevel programming and stochastic programming problems, while a meta-heuristic method, genetic algorithm, and Monte Carlo bounding techniques are used to solve the problems. Demand for PEVs is increasing with the growing concerns about environment pollution, energy resources, and the economy. However, battery capacity in PEVs is still limited and represents one of the key barriers to a more widespread adoption of PEVs. It is expected that drivers who have long-distance commutes hesitate to replace their internal combustion engine vehicles with PEVs due to range anxiety. To address this concern, PEV infrastructure can be developed to provide re-fully status when they are needed. This dissertation is primarily focused on the development of mathematical models that can be used to support decisions regarding a charging station location and installation problem. The major parts of developing the models included identification of the problem, development of mathematical models in the form of bilevel and stochastic programming problems, and development of a solution approach using a meta-heuristic method. PEV parking building problem was formulated as a bilevel programming problem in order to consider interaction between transportation flow and a manager decisions, while the charging station installation problem was formulated as a stochastic programming problem in order to consider uncertainty in parameters. In order to find the best-quality solution, a genetic algorithm method was used because the formulation problems are NP-hard. In addition, the Monte Carlo bounding method was used to solve the stochastic program with continuous distributions. Managerial implications and recommendations for PEV parking building developers and managers were suggested in terms of sensitivity analysis. First, in the planning stage, the developer of the PEV parking building should consider long-term changes in future traffic flow and locate a PEV parking building closer to the node with the highest destination trip rate. Second, to attract more parking users, the operator needs to consider the walkability of walking links.