A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions

The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health eco-nomic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appro-priate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles wer

Formal safety assessment of marine applications

This research has first established that it is based on multiple methodologies developed to tackle the areas of engineering cargo handling systems, both at port and on-board vessels, as well as in the area of organisational self-assessment. It continued in reviewing the current status and future aspects of marine safety assessment together with an examination of a few major accidents. The major problems identified in marine safety assessment in this research are associated with inappropriate treatment of uncertainty in data and human error issues during the risk modelling estimation process and the calculation of failure probabilities. Following the identification of the research needs, this thesis has developed several analytical models for the safety assessment of cargo handling systems and organisational assessment structure. Such models can be effectively integrated into a risk-based framework using the marine formal safety assessment, safety case concepts. Bayesian network (BN) and evidential reasoning (ER) approaches applicable to cargo handling engineering systems have been proposed for systematically and effectively addressing uncertainty due to randomness and vagueness in data respectively. ER test cases for both a vessel selection process and a comparison of the safety maturity of different organisations in terms of self-assessment have been produced within a domain in which main and sub criteria have been developed for assessment reasons a long with the combination of the proposed model with existing organisational models. BN test case for a Liquefied Petroleum Gas (LPG) reliquefaction plant has been produced within a cause-effect domain in which Bayes' theorem is the focal mechanism of inference processing. A methodology aiming in finding the probability of failure when having variables ruled by uncertainty is established using certain variable transformation methods through the First and Second order reliability methodologies. Form/Sorm produces a most likely failure point, which is demonstrated through the application at a port cargo handling crane system. The outcomes have the potential to facilitate the decision-making process in a risk-based framework. Finally, the results of the research are summarised and areas where further research is required to improve the developed methodologies are outlined

Probabilistic network models for cardiovascular monitoring

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 83-85).While treating patients during their hospital stay, physicians must frequently take into consideration massive amounts of clinical data. This data can come in many forms, such as continuous blood pressure tracings, intermittent laboratory results, or simple qualitative observations on the patient's appearance. Although access to such a rich collection of information is beneficial for making diagnoses and treatment decisions, it can sometimes be difficult for clinicians to mentally keep track of everything, especially in hectic environments such as hospital intensive care units (ICUs). In addition, there are certain physiological variables that cannot be measured noninvasively, but are critical indicators of a patient's state of health. One such example in cardiology is cardiac output - the mean flow rate of blood from the heart. In this thesis, we explore probabilistic networks as a method for integrating different types of clinical data into a single model, and as a vehicle for summarizing population statistics from medical databases. These networks can then be used to estimate unobservable variables of interest. We propose and test several networks of varying complexity on both a set of experimental porcine data, and a set of real ICU patient data. We find that continuous estimation of cardiac output is possible using probabilistic networks, and that the errors produced are comparable to those obtained from deterministic methods that employ the same in:Formation. Furthermore, since this technique is purely statistical in nature, it can be easily reformulated for applications where deterministic methods do not exist.by Shirley X. Li.M.Eng

Risk analysis of maritime accidents along the main route of the Maritime Silk Road: a Bayesian network approach

The safety of maritime transportation along the twenty-first century Maritime Silk Road (MSR) is important to ensure its development and sustainability. Maritime transportation poses risks of accidents that can cause the death or injury of crew members and damage to ships and the environment. This paper proposes a Bayesian network (BN) based risk analysis approach that is newly applied in the main route of the MSR to analyse its relevant maritime accidents. The risk data are manually collected from the reports of the accident that occurred along the MSR. Next, the risk factors are identified and the results from the modelling method can provide useful insights for accident prevention. Historical data collected from accident reports are used to estimate the prior probabilities of the identified risk factors influencing the occurrence of maritime accidents. The results show that the main influencing factors are the type and location of an accident and the type, speed, and age of the involved ship(s). In addition, scenario analysis is conducted to analyse the risks of different ships in various navigational environments. The findings can be used to analyse the probability of each possible maritime accident along MSR and to provide useful insights for shipowners’ accident prevention

Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

We propose an efficient family of algorithms to learn the parameters of a Bayesian network from incomplete data. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach provides consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM (as our approach requires no inference). Given sufficient data, we learn parameters that can be orders of magnitude more accurate

Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

Diffusion of Lexical Change in Social Media

Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified "netspeak" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311