An image‐guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool

Hargrave, Catriona; Deegan, Timothy; Bednarz, Tomasz; Poulsen, Michael; Harden, Fiona; Mengersen, Kerrie

doi:10.1002/mp.12979

Cited by 9 publications

(5 citation statements)

References 39 publications

(71 reference statements)

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“…DSS based on artificial intelligence techniques [10], Bayesian networks [11] and simulation modelling [12] have been applied for decision-making in health care, in general, and in MH care, in particular, [13–15]. Finally, DSS can assess the impact and/or effectiveness of MH policies [16], improving both the management of MH services [17–19] and care provision [20].…”

Section: Introductionmentioning

confidence: 99%

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

et al. 2019

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Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.

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Section: Introductionmentioning

confidence: 99%

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

et al. 2019

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“…In radiation oncology, BN models have been used for various purposes including diagnostic reasoning, meta-analysis of biomedical data, modeling, clinical decision support systems etc. [39,[42][43][44]. The DLORO ontology organizes radiation oncology domain knowledge into classsubclass structures and establishes dependency among domain concepts.…”

Section: Bayesian Network and Ontological Data Mappingmentioning

confidence: 99%

Ontologies in radiation oncology

et al. 2020

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“…In this way, they allow for easy juxtaposed comparison, either across time or across patients. Hargrave et al [HDB*18] propose an image‐guided decision support framework incorporating a Bayesian network and visualization tool for online cone‐beam computed tomography (CBCT)‐based image‐guided radiotherapy for prostate cancer patients. The Bayesian network represents the relationships between pelvic organ volume variations, an image feature alignment score, delivered dose, treatment plan compliance, intra‐fraction motion, contouring and couch shift errors.…”

Section: Taxonomy and Presentation Of Previous Work In Vc For Rtmentioning

confidence: 99%

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

Schlachter

Raidou

Muren

et al. 2019

Computer Graphics Forum

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Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.

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An image‐guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool

Cited by 9 publications

References 39 publications

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

Ontologies in radiation oncology

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

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