Essays on the management of appointments for chronic conditions

Treating chronic conditions is a fairly complex task, which requires well-timed appointments to control one's disease progression. In my dissertation I would like to optimize the monitoring strategies and better predict the demand-for-care of patients with chronic kidney disease (CKD). To do that I design a chronic disease monitoring framework which consists of forecasting, survival analysis and Markov Decision Process (MDP) models. First, I propose a forecasting model which quantifies the impact of CKD-related doctor's appointments on patient's disease progression. The model accounts for patient's comorbidities, vital signs, and important laboratory values. Second, I propose a survival analysis model, which estimates the expected life days of a patient given his or her current health status. Finally, I use the information gained from the first two models to parametrize and solve the MDP, which can suggest monitoring strategies and predict medium-term demand for CKD-patient-care in a clinic. In addition to the chronic disease monitoring framework, I examine CKD patient characteristics associated with a higher resource utilization

Trajectory Data Mining in Mouse Models of Stroke

Contains fulltext : 273912.pdf (Publisher’s version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p

Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique

The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ``learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ``real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data

Bayesian probability encoding in medical decision analysis

Ph.DDOCTOR OF PHILOSOPH

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Endovascular Treatment of Ischemic Stroke: Treat the right patient, at the right time, in the right place

The effect of endovascular treatment for ischemic stroke varies between individual patients and is highly time-dependent. The overall aim of this thesis was to increase the benefit of endovascular treatment by optimizing prediction of outcome and treatment effect (Part I), reducing treatment delay (Part II), and improving prehospital triage strategies (Part III)

Beyond ten-year risk: novel approaches to the primary prevention of cardiovascular disease

In cost-effectiveness analysis, outcomes are typically averaged across large groups to represent a patient population. Implementation and reimbursement decisions based on such analyses often ignore considerable heterogeneity in cost-effectiveness between patients. While good practice guidance for economic evaluations suggest including subgroup analysis, in practice this is frequently overlooked or underutilised. This thesis shows that failing to adequately represent heterogeneity in decision-making leads to an inefficient distribution of healthcare resources. This theory is applied in a study of cholesterol-reducing medication for the primary prevention of cardiovascular disease (CVD). Despite improvements in recent years, CVD remains a significant cause of mortality, morbidity, and health inequality around the world. Rates of the disease have begun to plateau in recent years and novel approaches to its prevention are required. Cholesterol reduction for the primary prevention of cardiovascular disease is a clinical area where better reflection of heterogeneity in cost-effectiveness could significantly improve current practice. Statins are a widely prescribed cholesterol-reducing medication which have recently come off patent. This has led them to become cheaper and cost-effective in a large proportion of CVD-free populations in high-income countries. PCSK9 inhibitors are a more expensive and more effective cholesterol-reducing medication. For both treatments, decision-makers must establish which groups they will prioritise for treatment. Through epidemiologic and health economic analysis, this thesis aims to establish optimal approaches for prioritising patients for cholesterol-reducing therapy. Preventive statin therapy is typically targeted at individuals estimated to have a high ten-year risk of developing CVD. However, individuals with the same ten-year risk may experience different outcomes from preventive treatment. The epidemiologic bases for three alternative approaches to the CVD prevention are discussed. These are: (i) continued use of ten-year risk scoring, (ii) novel decision mechanisms which incorporate ten-year risk, and (iii) direct use of decision-analytic models in clinical practice to guide treatment decisions. Several treatment policies may be characterised by one of the aforementioned approaches to prevention. These include: lowering the risk threshold for treatment initiation, improving the discrimination of risk scores with novel biomarker testing, age-stratified risk thresholds, absolute risk reduction-guided therapy, and outcome maximisation with decision-analytic models. Decision-analytic modelling was employed to assess the long-term effectiveness and cost-effectiveness of these policies. Additional analysis showed how decision-makers can signal demand for PCSK9 inhibitors and achieve welfare gains by reflecting heterogeneity in their decision-making. This thesis demonstrates the importance of reflecting heterogeneity in cost-effectiveness. It shows that standard care regarding the primary prevention of CVD often ignores heterogeneity, leading to suboptimal decision-making. This holds true for long-established, inexpensive treatments like statin therapy and novel, expensive treatments like PCSK9 inhibitors