A decision support system for boosting warfarin maintenance dose using fuzzy logic

Background: Warfarin is the most common oral anticoagulant. This drug is used for the prevention and treatment of thromboembolic patients. It is difficult for physician to predict the results of warfarin prescriptions because there is narrow boundary between therapeutic range and complications of warfarin. Therefore drug dose adjustment is normally performed by an expert physician. Decision support systems that use extracted knowledge from experts in the field of drug dose adjustment would be useful in reducing medical errors, especially in the clinics with limited access to experts. The aim of this study was to propose a method for boosting the maintenance dose of warfarin for a maximum period of three days to eliminate disruptions in International Normalized Ratio (INR). Methods: In a retrospective study, from December 2013 to February 2014 in Shahid Rajaee Heart Center, Tehran, Iran, 84 patients with International Normalized Ratio below (INR) the therapeutic range was selected who was undergone a boosting dose during three days. Patients with unstable maintenance dose were excluded from the study. In this study, data from 75 patients receiving warfarin therapy were used for developing and evaluation of the proposed model. The INR target range for 37 patients out of remaining 75 cases was between 2.5 and 3.5, while for 38 patients the intended INR range was between 2 and 3. A separate fuzzy model was designed for each of the above-mentioned therapeutic ranges. Results: The recommended dose for 37 patients having INR therapeutic range of 2.5 to 3.5 has mean absolute error and root mean squared error of 1.89 and 2.78 respectively for three days. These error rates are 1.97 and 2.88 respectively for 38 patients who are in therapeutic range 2 to 3. Conclusion: The results are promising and encourage one to consider this system for more study with the aim of possible use as a decision support system in the future. © 2015, Tehran University of Medical Sciences. All rights reserved

Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from data, but where we wish to explicitly model the impact of some additional expert variable (for which there is expert judgment but no data). Because the statistical outcomes are already influenced by the causes an expert might identify as variables missing from the dataset, the incentive here is to add the expert factor to the model in such a way that the distribution of the data variable is preserved when the expert factor remains unobserved. We provide a method for eliciting expert judgment that ensures the expected values of a data variable are preserved under all the known conditions. We show that it is generally neither possible, nor realistic, to preserve the variance of the data variable, but we provide a method towards determining the accuracy of expertise in terms of the extent to which the variability of the revised empirical distribution is minimised. We also describe how to incorporate the assessment of extremely rare or previously unobserved events

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

OBJECTIVES: 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way

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

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

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice

Decision support system for Warfarin therapy management using Bayesian networks

Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy management. The DSS is developed in collaboration with a Swedish hospital group that manages Warfarin therapy for more than 3000 patients. The proposed model can assist the clinician in making dose-adjustment and follow-up interval decisions, investigating variation causes, and evaluating bleeding and thrombosis risks related to therapy. The model is built upon previous findings from medical literature, the knowledge of domain experts, and large dataset of patients

Bayesian Networks for Evidence Based Clinical Decision Support.

PhDEvidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may not be directly applicable to individual patients. For example, randomised controlled trials (RCT) often exclude patients with comorbidities, so a clinician has to combine the results of the RCT with evidence about comorbidities using his clinical knowledge of how disease, treatment and comorbidities interact with each other. Bayesian networks (BN) are well suited for assisting clinicians making evidence-based decisions as they can combine knowledge, data and other sources of evidence. The graphical structure of BN is suitable for representing knowledge about the mechanisms linking diseases, treatments and comorbidities and the strength of relations in this structure can be learned from data and published results. However, there is still a lack of techniques that systematically use knowledge, data and published results together to build BNs. This thesis advances techniques for using knowledge, data and published results to develop and refine BNs for assisting clinical decision-making. In particular, the thesis presents four novel contributions. First, it proposes a method of combining knowledge and data to build BNs that reason in a way that is consistent with knowledge and data by allowing the BN model to include variables that cannot be measured directly. Second, it proposes techniques to build BNs that provide decision support by combining the evidence from meta-analysis of published studies with clinical knowledge and data. Third, it presents an evidence framework that supplements clinical BNs by representing the description and source of medical evidence supporting each element of a BN. Fourth, it proposes a knowledge engineering method for abstracting a BN structure by showing how each abstraction operation changes knowledge encoded in the structure. These novel techniques are illustrated by a clinical case-study in trauma-care. The aim of the case-study is to provide decision support in treatment of mangled extremities by using clinical expertise, data and published evidence about the subject. The case study is done in collaboration with the trauma unit of the Royal London Hospital

Anthelmintic resistance in equine parasites: an epidemiological approach to build a framework for sustainable parasite control

Faecal egg count (FEC) directed targeted anthelmintic treatment programmes and regular efficacy testing using the faecal egg count reduction test (FECRT) have been advocated to support evidence-based helminth control in horses. One major hurdle to their widespread application is that horse owners/managers and those that prescribe anthelmintics may have insufficient knowledge on which to base evidence-based protocols. The ultimate aim of this study was to create a framework for a decision support system (DSS) to support evidence-based helminth control in horses. To create the framework, the diagnostic performance of FEC and FECRT methodologies were evaluated. In addition, the efficacy of the three licensed anthelmintic classes was tested in several equine populations. The prevalence and distribution of helminths was determined in these populations, and an analysis undertaken to investigate factors associated with different levels of strongyle egg shedding in individuals. The consistency of egg shedding patterns in individuals over time was evaluated and the resource implications of following a FEC directed targeted treatment investigated. The FEC analysis findings support the rationale of FEC directed targeted anthelmintic treatments in horses to reduce treatment frequency in order to mitigate the impact of anthelmintic resistance. Moreover, the results show that such a strategy may be cost effective. The efficacy studies revealed that the macrocyclic lactone anthelmintics were highly effective in reducing strongyle egg output at two weeks after treatment, but further studies are required to analyse the strongyle egg reappearance period after treatment with these anthelmintics. In summary, this study validates the use of FEC directed treatment protocols in the field and the next step will be to use the derived information to design user-friendly online support tools