Issues in the Probability Elicitation Process of Expert-Based Bayesian Networks

Nunes, João Arriscado; Barbosa, Mirko; Silva, Luiz; Gorgônio, Kyller Costa; Almeida, Hyggo; Perkusich, Angelo

doi:10.5772/intechopen.81602

Cited by 7 publications

(8 citation statements)

References 29 publications

(56 reference statements)

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“…There exists a wealth of literature concerning the elicitation of BNs and associated probabilities [ 14 , 15 , 24 - 28 ]. However, we are not aware of previous attempts to elicit comparably sized BNs with such limited time constraints on experts.…”

Section: Discussionmentioning

confidence: 99%

“…To be clear, this focus on characterization rather than diagnosis was deliberately taken owing to the lack of accurate diagnostic labels for the majority of people with LBP [ 13 ], but the BN was designed to identify patients with serious diagnoses, such as nerve root pain and cauda equina syndrome, where these can be identified. For reasons mentioned above, the clinical knowledge base of our BN must be elicited from experts [ 14 , 15 ]. We also believe that it is important not to constrain the type of LBP considered but use expert opinion to guide our focus.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

Hill¹,

Joyner²,

Keith-Jopp³

et al. 2021

JMIR Res Protoc

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Background Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts. Objective This paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. Methods We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network. Results Ethical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity. Conclusions We have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions. International Registered Report Identifier (IRRID) DERR1-...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

Hill¹,

Joyner²,

Keith-Jopp³

et al. 2021

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

“…There exists a wealth of literature concerning the elicitation of BNs and associated probabilities [14,15,[24][25][26][27][28]. However, we are not aware of previous attempts to elicit comparably sized BNs with such limited time constraints on experts.…”

Section: Methodology For Eliciting Expert Opinionmentioning

confidence: 99%

“…To be clear, this focus on characterization rather than diagnosis was deliberately taken owing to the lack of accurate diagnostic labels for the majority of people with LBP [13], but the BN was designed to identify patients with serious diagnoses, such as nerve root pain and cauda equina syndrome, where these can be identified. For reasons mentioned above, the clinical knowledge base of our BN must be elicited from experts [14,15]. We also believe that it is important not to constrain the type of LBP considered but use expert opinion to guide our focus.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study (Preprint)

Hill¹,

Joyner²,

Keith-Jopp³

et al. 2020

Preprint

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BACKGROUND Low back pain is an increasingly burdensome condition for patients and health professionals alike, and studies show that persistent pain and disability is on the increase. Previous decision support tools for management of LBP have focussed on a subset of factors due to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from UK government to introduce this technology in the NHS, there is an opportunity to develop a more comprehensive decision support tool. We propose to do this with a Bayesian Network, which will entail an expert elicited clinical reasoning model. OBJECTIVE This paper proposes a method for conducting a modified RAND Appropriateness procedure to elicit a Bayesian Network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure. METHODS We propose to recruit expert clinicians with a special interest in LBP from physiotherapy, general practice and sports medicine. The procedure consists of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face to face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face to face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian Network. Stage 4 is a rudimentary validation of the Bayesian Network. RESULTS Ethical approval has been gained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. Lessons learned have included the need for a bespoke, online elicitation tool, cognitive bias training, and development of methodology for reducing the elicitation burden on participants. CONCLUSIONS The internal pilot study has yielded a clinically credible model of clinical reasoning in LBP. We believe that this method will enable us to build an AI capable Bayesian Network representative of expert clinical reasoning in low back pain.

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“…Figure 3. A simple BN model indicating the inter-dependencies between a lung cancer classifier and the affecting risk factors (adopted from[47]). …”

mentioning

confidence: 99%

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

Sedighi

Varga²,

Hosseinian-Far

et al. 2021

IJERPH

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The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

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Issues in the Probability Elicitation Process of Expert-Based Bayesian Networks

Cited by 7 publications

References 29 publications

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study (Preprint)

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks

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