Dynamic Decision Support System Based on Bayesian Networks

2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), 2014

2012

This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This approach does not capture the full uncertainty surrounding the prior knowledge. Secondly, it demonstrates a novel use of the multinomial BN formulation in learning parameters of categorical variables. The traditional approach requires fixed number of parameters during the learning process but this framework allows an analyst to generate a multinomial BN model based on the number of parameters requi...

2005

Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resources becomes critical with the aging of the population. The treatment of renal failure is an example where telemedicine can help to increase care quality. Over the last decades Bayesian networks has become a popular representation for encoding uncertain expert knowledge. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. We developed a dynamic Bayesian network adapted to the monitoring of the dry weight of patients suffering from chronic renal failure treated by hemodialysis. An experimentation conducted at dialysis units indicated that the system is reliable and gets the approbation of its users.

Applied Intelligence, 2005

Diagnosis and prediction m some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel represen tation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relation. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and event prediction with good results. 1. INTRODUCTION Artificial intelligence techniques are entering real world domains, such as medicine, industrial diagnosis, communications, planning, financial forecasting and scheduling. These applications have revealed a great need for powerful methods for knowledge representation. In particular, the evolutionary nature of these domains requires a representation that takes into account temporal information. The exact timing information for things like lab-test results, occurrence of symptoms, observations, measures, as well as faults, can be crucial in these kinds of applications.

Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018

The task of carrying out an effective and efficient decision on medical domain is a complex one, since a lot of uncertainty and vagueness is involved. Fuzzy logic and probabilistic methods for handling uncertain and imprecise data both provide an advance towards the goal of constructing an intelligent decision support system (DSS) for medical diagnosis and therapy. This work reports on a successfully developed DSS concerning pneumonia disease. A detailed and clear description of the reasoning behind the core decision making module of the DSS, is included, depicting the proposed methodological issues. The results have shown that the suggested methodology for constructing bayesian networks (BNs) from fuzzy rules gives a front-end decision about the severity of pulmonary infections, providing similar results to those obtained with physicians' intuition.

2014

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

International Symposium on Medical Data Analysis, 2001

Abstract. Due to the uncertain nature of many,of the factors that in- fluence on the performance of an emergency medical service, we propose using Bayesian networks to model this kind of systems. We use an al- gorithm for learning Bayesian networks to build the model, from the point of view of a hospital manager, and apply it to the specific

Using Bayesian Networks for Risk Assessment in Healthcare System, 2019

To ensure patient safety, the healthcare service must be of a high quality, safe and effective. This work aims to propose integrated approaches to risk management for a hospital system. To improve patient’s safety, we should develop methods where different aspects of risk and type of information are taken into consideration. The first approach is designed for a context where data about risk events are available. It uses Bayesian networks for quantitative risk analysis in the hospital. Bayesian networks provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. The methodology is used to analyze the patient’s safety risk in the operating room, which is a high risk area for adverse event. The second approach uses the fuzzy Bayesian network to model and analyze risk. Fuzzy logic allows using the expert’s opinions when quantitative data are lacking and only qualitative or vague statements can be made. This approach provides an actionable model that accurately supports human cognition using linguistic variables. A case study of the patient’s safety risk in the operating room is used to illustrate the application of the proposed method.

2016

BFs to analyse the impact of Diagnosis on Number of meetings The analysis of observational data requires the use of a model, such as a multivariate regression. Bayesian networks (BNs) are well known as expert systems but can also be used to model data. A BN is a probabilistic model that represents the probabilistic relationships and conditional dependencies among variables. A BN allows probabilistic inference to be performed coherently, using the law of probability. Also a BN has the from the Barts and the London HPB (HepatoPancreaticoBiliary) centre following some changes to the MDT process. By evaluating the strength of each of the associations, we examine whether the MDT process has improved treatment recommendations for these patients.. 8 1.2 Structure of this thesis Chapter 2 discusses the potential benefits of Bayesian methods for introducing new changes in health service. We review the existing approaches to examine the effectiveness of complex health care initiatives and discuss the pitfalls of these approaches. Chapter 3 introduces BNs and reviews existing methods for their construction, including both expert judgement and learning from data. The importance of dynamic

Expert Systems with Applications, 2009

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive care units is seen as a clinical challenge. The difficulty in diagnosing ventilator-associated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and treating patients with pneumonia, a decision-theoretic network had been designed with the help of domain experts. A major limitation of this network is that it does not represent pneumonia as a dynamic process that evolves over time. In this paper, we construct a dynamic Bayesian network that explicitly captures the development of the disease over time. We discuss how probability elicitation from domain experts served to quantify the dynamics involved and how the nature of the patient data helps reduce the computational burden of inference. We evaluate the diagnostic performance of our dynamic model for a number of real patients and report promising results.