Clinical evidence framework for Bayesian networks

Yet, Barbaros; Perkins, Zane; Tai, Nigel; Marsh, William

doi:10.1007/s10115-016-0932-1

Cited by 17 publications

(20 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system comprises a binary finite state machine, the training stage of which allows for subject-specific personalization of its underlying predictive model, which triggers warnings and alarms as the health status evolves over time. Yet et al [11] introduced a framework for representing the evidence-base of a Bayesian network (BN) decision support model, aiming to present all the clinical evidence alongside the BN itself (i.e. supporting and conflicting evidence, as well as evidence associated with relevant but excluded factors).…”

Section: Discussion and Outlookmentioning

confidence: 99%

Contributions from the 2017 Literature on Clinical Decision Support

Koutkias

Bouaud

2018

Yearb Med Inform

View full text Add to dashboard Cite

The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that "Big Data" and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.

show abstract

Section: Discussion and Outlookmentioning

confidence: 99%

Contributions from the 2017 Literature on Clinical Decision Support

Koutkias

Bouaud

2018

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…-Explanation of the model: 'it consists of displaying (verbally, graphically or in frames) the information contained in the knowledge base' [6]. In other words, we explain how the structure and parameters of the model relate to domain knowledge [17]. -Explanation of reasoning: 'it may provide three kinds of justification; (1) the results obtained by the system and the reasoning process that produced them, (2) the results not obtained by the system, despite the user's expectations, (3) hypothetical reasoning' [6].…”

Section: Explanation In Bayesian Networkmentioning

confidence: 99%

“…Several variants of this approach have been proposed. For instance, Yap et al [17] shift the explanation entirely to the MB; this idea has been incorporated in our approach, but we use it alongside the evidence variables.…”

Section: Related Work On Explanation Of Reasoning In a Bnmentioning

confidence: 99%

An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making

Kyrimi

Mossadegh

Tai

et al. 2020

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to 'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are: (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted. This paper considers Bayesian Networks (BNs): directed acyclic graphs, showing causal or influential relationships between random variables. These variables can be discrete or continuous, and a BN with both is called `hybrid'. The uncertain relationships between connected variables are expressed using conditional probabilities. The strength of these relationships is captured in the Node Probability Table (NPT), used to represent the conditional probability distribution of each node in the BN given its parents. Once values for all NPTs are given, the BN is fully parameterized, and probabilistic reasoning (using Bayesian inference) can be performed. However, the reasoning process is not always easy for a user to follow [6], [7], [8].In contrast to many CDS models, a BN is not a black box and its reasoning can be explained [6], [9]. Several approaches have been proposed to explain the reasoning of a BN (presented in Section 3). However, there are many situations where these methods cannot be applied. First, most of the methods are applied to BNs that include only discrete variables. Some of them are even restricted to binary variables only. However, most of the medical BNs include continuous nodes as well. In addition, most of them try to find the best explanation that can be time-consuming, especially for large BNs, which are common in medical applications. Finally, in some methods, user input is required in different stages of the explanation. This can be problematic, especially in situations where there is time pressure.In this paper, we propose a practical method of explaining the reasoning in a BN, so that a user can understand how a prediction is generated. The method is an extension of a previous conference paper published by the authors [10]. Our proposed method can be used in hybrid networks that have both continuous and discrete nodes and requires no user input. In addition, we simplify the process of identifying the most important evidence and chains of reasoning, so we can rapidly produce a good and concise explanation, but not necessarily the most complete one. In fact, our method produces an incremental explanation that has three successive l...

show abstract

“…To point out relevant works addressing the visualisation and presentation of single machine learning agents, Yet et al [160] have presented a GUI for browsing the evidence and output of a Bayesian Networks (BN) implementation. Their framework helps health care professionals to understand the evidence that is used in the decision process of a BN reasoning.…”

Section: Guis For Providing Better Understanding Of Autonomic Networkmentioning

confidence: 99%

“…The comparisons are based on contextual metadata and filtering mechanisms to allow users to dynamically examine the performances [157,57,34]. Moreover, similar design for interactive exploration and semantically structurized representation of a single agent behaviour (as presented in CONTRIB2.2) has been earlier applied for machine learning agents in order to make humans understand their decisions [160,162,78].…”

Section: Guis For Providing Better Understanding Of Autonomic Networkmentioning

confidence: 99%