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

A mathematical modeling-based method to estimate utility function weights in multiple criteria decision making problems

Çok Kriterli Karar Verme (ÇKKV) problemlerindeki temel bir konu, karar vericinin (KV) tercihlerinin problem çözme sürecine dâhil edilmesidir. Birçok ÇKKV yöntemi, KV tercihlerinin fayda fonksiyonları yoluyla modellenebileceğini varsaymaktadır. Bu fonksiyonların parametre değerleri farklı KV’lerin problemle ilgili farklı önceliklerini ortaya koymaktadır. Literatürdeki çok sayıda yaklaşım, bu parametrelerin baştan bilindiğini kabul etmekte veya KV’nin bunları doğru bir şekilde doğrudan ifade edebileceğini varsaymaktadır. Tercih parametrelerini elde etmek için geliştirilen yöntemler ise KV’nin çok sayıda değerlendirme ve karşılaştırma yapmasını gerektirebilmekte ve karmaşık süreçler içerebilmektedir. Bu çalışmada geliştirdiğimiz matematiksel programlama temelli yöntem, ağırlıklı toplam şeklinde ifade edilen fayda fonksiyonlarının kriter ağırlıklarını KV için bilişsel zorluk yaratmayacak az sayıda tercih değerlendirmesi ile tahmin etmektedir. KV’den direkt olarak kriterleri değerlendirmesi istenmemekte, sınırlı sayıda karar alternatifini tercih sırasına sokması beklenmektedir. Geliştirilen yöntem, üç kriterli finansal portfolyo seçimi problemine ve beş kriterle değerlendirilen dünya üniversitelerinin sıralanması problemine uygulanmıştır. Karşılaştırma yapmak amacıyla literatürde kullanılan başka bir ağırlık tahmini yöntemi de (Swing yöntemi) aynı problemlere uygulanmıştır. Geliştirdiğimiz yaklaşımın bu yöntemden daha kullanışlı olduğu, daha az bilişsel yük getirdiği ve daha iyi sonuçlar verdiği gözlemlenmiştir.A basic issue in Multiple Criteria Decision Making (MCDM) problems is to include the preferences of the decision maker (DM) in the problem solution process. Many MCDM methods assume that DM preferences can be modeled in the form of utility functions. The parameters of these functions represent varying priorities of different DMs about the problem. Several approaches in the literature assume that these parameters are already known or the DM can express them directly and correctly. The approaches developed to derive preferential parameters may require the DM to make many assessments and comparisons, and involve complex procedures. The mathematical programming-based method developed in this study estimates criteria weights in weighted sum utility functions by few preference assessments without imposing cognitive difficulty on the DM. The DM is not asked to directly evaluate criteria but to rank a limited number of alternatives in preference order. The developed approach is applied to a financial portfolio selection problem with three criteria and a university ranking problem with five criteria. For comparison, the Swing method is also applied to the same problems. The proposed method is observed to be more convenient, impose less cognitive burden and provide superior results

How to model mutually exclusive events based on independent causal pathways in Bayesian network models

This is supported by ERC project ERC-2013-AdG339182-BAYES_KNOWLEDGE

Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)

Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II

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

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

A framework to present Bayesian networks to domain experts and potential users

Knowledge and assumptions behind most Bayesian network models are often not clear to anyone other than their developers. This limits their use as decision support tools in clinical and legal domains where the outcomes of decisions can be critical. We propose a framework for representing knowledge supporting or conflicting with BN, and knowledge associated with factors that are relevant but excluded from the BN. The aim of this framework is to enable domain experts and potential users to browse, review, criticise and modify a BN model without having deep technical knowledge about BNs.Co-authors: Zane Perkins (Queen Mary University of London), Nigel Tai (The Royal London Hospital), William Marsh (Queen Mary University of London

Bayes ağları ile futbol analitiği: FutBA modeli

Futbol maçları yüksek belirsizliğe sahiptir ve sonuçlarının tahmin edilmesi zordur. Sadece veriye dayalı tahmin ve yapay öğrenme yöntemleri futbol tahminlerinde kısıtlı performans elde edebilmektedir. Uzman bilgisine dayalı modeller başarıya sahip olmuştur, fakat bu modellerin başka yerlere uygulanması için yine uzman bilgisi ve analistler tarafından gözden geçirilmesi gerekmektedir. Bu çalışmada Türkiye futbol ligleri için geliştirilmiş özgün bir Bayes ağı modeli önerilmektedir. Önerilen model futbol müsabakası yapan takımların hücum ve savunma gücünü maça ilişkin birçok gözlem ile belirleyerek maç sonucunu tahmin etmeyi amaçlamaktadır. Modelin yapısı ve parametreleri uzman bilgisi ile geliştirilmiştir. Modelden tahmin üretirken geçmiş maç verisi ile maça ilişkin uzman bilgisi girdi olarak kullanılabilmektedir. Önerilen model Türkiye Süper Ligi’nden gerçek maç verisi ile değerlendirilmiştir

Decision Support for Project Management by using Diagnostic Inference and Explaining-Away in Bayesian Networks

Risk and uncertainty are natural elements of projects. A project manager has to manage these elements effectively to improve the chance of project success. Yet, well-known biases and heuristics about decision-making under uncertainty limit a project manager's ability to deal with these elements, especially for complex projects, and decision support tools can be helpful for this task. This paper focuses on building a decision support model for reasoning with uncertainty in project control. We propose a Bayesian Network (BN) model that uses the notions of diagnostic reasoning and 'explaining-away' to infer the effort requirements, progress and risks of project tasks. The proposed model aids the project manager in reasoning with uncertainty and risk factors when monitoring project progress. We use a project example to demonstrate the model structure, its use, benefits and limitation compared to conventional project control tools