Barbaros Yet

A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study

We focus on project cost, benefit and risk analysis.We propose a modelling framework that uses a hybrid and dynamic Bayesian network(BN).BN offers unique features of analysing risk scenarios and budget policies.It uses uncertainty and variability of risk and economic factors in its predictions.The framework is illustrated by a case study of agricultural development projects. Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.

Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma

Lower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision‐making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT.

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences

OBJECTIVES Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. METHOD The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. RESULTS The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). CONCLUSIONS We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.

Clinical evidence framework for Bayesian networks

There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.

Using operational data for decision making: a feasibility study in rail maintenance

Abstract In many organisations, large databases are created as part of the business operation: the promise of ‘big data’ is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts’ knowledge is vital for the model’s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.

The role of splenic angioembolization as an adjunct to nonoperative management of blunt splenic injuries: A systematic review and meta-analysis

BACKGROUND Nonoperative management (NOM) of hemodynamically normal patients with blunt splenic injury (BSI) is the standard of care. Guidelines recommend additional splenic angioembolization (SAE) in patients with American Association for the Surgery of Trauma (AAST) Grade IV and Grade V BSI, but the role of SAE in Grade III injuries is unclear and controversial. The aim of this systematic review was to compare the safety and effectiveness of SAE as an adjunct to NOM versus NOM alone in adults with BSI. METHODS A systematic literature search (Medline, Embase, and CINAHL) was performed to identify original studies that compared outcomes in adult BSI patients treated with SAE or NOM alone. Primary outcome was failure of NOM. Secondary outcomes included morbidity, mortality, hospital length of stay, and transfusion requirements. Bayesian meta-analyses were used to calculate an absolute (risk difference) and relative (risk ratio [RR]) measure of treatment effect for each outcome. RESULTS Twenty-three studies (6,684 patients) were included. For Grades I to V combined, there was no difference in NOM failure rate (SAE, 8.6% vs NOM, 7.7%; RR, 1.09 [0.80–1.51]; p = 0.28), mortality (SAE, 4.8% vs NOM, 5.8%; RR, 0.82 [0.45–1.31]; p = 0.81), hospital length of stay (11.3 vs 9.5 days; p = 0.06), or blood transfusion requirements (1.8 vs 1.7 units; p = 0.47) between patients treated with SAE and those treated with NOM alone. However, morbidity was significantly higher in patients treated with SAE (SAE, 38.1% vs NOM, 18.6%; RR, 1.83 [1.20–2.66]; p < 0.01). When stratified by grade of splenic injury, SAE significantly reduced the failure rate of NOM in patients with Grade IV and Grade V splenic injuries but had minimal effect in those with Grade I to Grade III injuries. CONCLUSION Splenic angioembolization should be strongly considered as an adjunct to NOM in patients with AAST Grade IV and Grade V BSI but should not be routinely recommended in patients with AAST Grade I to Grade III injuries. LEVEL OF EVIDENCE Systematic review and meta-analysis, level III.

An Improved Method for Solving Hybrid Influence Diagrams

Abstract While decision trees are a popular formal and quantitative method for determining an optimal decision from a finite set of choices, for all but very simple problems they are computationally intractable. For this reason, Influence Diagrams (IDs) have been used as a more compact and efficient alternative. However, most algorithmic solutions assume that all chance variables are discrete, whereas in practice many are continuous. For such ‘Hybrid’ IDs (HIDs) the current-state-of-the-art algorithms suffer from various limitations on the kinds of inference that can be performed. This paper presents a novel method that overcomes a number of these limitations. The method solves a HID by transforming it to a Hybrid Bayesian Network (HBN) and carrying out inference on this HBN using Dynamic Discretization (DD). It generates a simplified decision tree from the propagated HBN to compute and present the optimal decisions under different decision scenarios. To provide satisfactory performance the method uses ‘inconsistent evidence’ to model functional and structural asymmetry. By using the entire marginal probability distribution of the continuous utility and chance nodes, rather than expected values alone, our method also enhances decision analysis by offering the possibility to consider additional statistics other than expected utility, such as measures of risk. We illustrate our method by using the oil wildcatter example and its variations with continuous nodes. We also use a financial score to combine risk and return measures, for illustration.

COTTAPP: An Online University Timetable Application based on a Goal Programming Model

Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering.