Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

Discovery of Type 2 Diabetes Trajectories from Electronic Health Records

University of Minnesota Ph.D. dissertation. September 2020. Major: Health Informatics. Advisor: Gyorgy Simon. 1 computer file (PDF); xiii, 110 pages.Type 2 diabetes (T2D) is one of the fastest growing public health concerns in the United States. There were 30.3 million patients (9.4% of the US populations) suffering from diabetes in 2015. Diabetes, which is the seventh leading cause of death in the United States, is known to be a non-reversible (incurable) chronic disease, leading to severe complications, including chronic kidney disease, amputation, blindness, and various cardiac and vascular diseases. Early identification of patients at high risk is regarded as the most effective clinical tool to prevent or delay the development of diabetes, allowing patients to change their life style or to receive medication earlier. In turn, these interventions can help decrease the risk of diabetes by 30-60%. Many studies have been conducted aiming at the early identification of patients at high risk in the clinical settings. These studies typically only consider the patient's current state at the time of the assessment and do not fully utilize all available information such as patient's medical history. Past history is important. It has been shown that laboratory results and vital signs can differ between diabetic and non-diabetic patients as many as 15-20 years before the onset of diabetes. We have also shown in our study that the order in which patients develop diabetes-related comorbidities is predictive of their diabetes risk even after adjusting for the severity of the comorbidities. In this thesis, we develop multiple novel methods to discover T2D trajectories from Electronic Health Records (EHR). We define trajectory as an order of in which diseases developed. We aim to discover typical and atypical trajectories where typical trajectories represent predominant patterns of progressions and atypical trajectories refer to the rest of the trajectories. Revealing trajectories can allow us to divide patients into subpopulations that can uncover the underlying etiology of diabetes. More importantly, by assessing the risk correctly and by a better understanding of the heterogeneity of diabetes, we can provide better care. Since data collected from EHR poses several challenges to directly identify trajectories from EHR data, we devise four specific studies to address the challenges: First, we propose a new knowledge-driven representation for clinical data mining, second, we demonstrate a method for estimating the onset time of slow-onset diseases from intermittently observable laboratory results in the specific context of T2D, third, we present a method to infer trajectories, the sequence of comorbidities potentially leading up to a particular disease of interest, and finally, we propose a novel method to discover multiple trajectories from EHR data. The patterns we discovered from above four studies address a clinical issue, are clinically verifiable and are amenable to deployment in practice to improve the quality of individual patient care towards promoting public health in the United States

Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring

Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains.Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones.To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases.Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains

Bayesian Networks for Clinical Decision Making : Support, Assurance, Trust

PhD thesisBayesian networks have been widely proposed to assist clinical decision making. Their popularity is due to their ability to combine different sources of information and reason under uncertainty, using sound probabilistic laws. Despite their benefit, there is still a gap between developing a Bayesian network that has a good predictive accuracy and having a model that makes a significant difference to clinical decision making. This thesis tries to bridge that gap and proposes three novel contributions. The first contribution is a modelling approach that captures the progress of an acute condition and the dynamic way that clinicians gather information and take decisions in irregular stages of care. The proposed method shows how to design a model to generate predictions with the potential to support decision making in successive stages of care. The second contribution is to show how counterfactual reasoning with a Bayesian network can be used as a healthcare governance tool to estimate the effect of treatment decisions other than those occurred. In addition, we extend counterfactual reasoning in situations where the targeted decision and its effect belong to different stages of the patient’s care. The third contribution is an explanation of the Bayesian network’s reasoning. No model is going to be used if it is unclear how it reasons. Presenting an explanation, alongside a prediction, has the potential to increase the acceptability of the network. The proposed technique indicates which important evidence supports or contradicts the prediction and through which intermediate variables the information flows. The above contributions are explored using two clinical case studies. A clinical case study on combat trauma care is used to investigate the first two contributions. The third contribution is explored using a Bayesian network developed by others to provide decision support in treating acute traumatic coagulopathy in the emergency department. Both case studies are done in collaboration with the Royal London Hospital and the Royal Centre for Defence Medicine