Decision-making in oesophageal cancer MDTs: model vs clinician views of factor importance, and clinician perception of an ML tool
Decision-making in oesophageal cancer MDTs: model vs clinician views of factor importance, and clinician perception of an ML tool
Treatment decisions for oesophageal cancer (OC) are multifaceted. A study combining a survey of clinicians with mixed methods and a comparison to a machine learning (ML) model was conducted to investigate the factors influencing these decisions. The study found that age and gender played a more significant role in the ML model than they did in the clinicians' conscious decision-making process. Additionally, we identified a variety of other important factors, such as specific tests and symptoms, social circumstances, and nutritional status. The prospect of utilising an ML-based decision support tool in the future received generally positive feedback, although opinions varied widely. Challenges to its adoption include perceptions of clinician superiority, the uniqueness of patient cases, the need for transparency and safeguards in the model, the requirements for data input, and the demand for proven effectiveness.
Webb, Catherine
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Thavanesan, Navamayooran
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Dewar-Haggart, Rachel
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Naiseh, Mohammad
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Underwood, Tim
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Vigneswaran, Ganesh
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March 2024
Webb, Catherine
a4921979-8d4a-4b78-abea-16cc885f965e
Thavanesan, Navamayooran
94f0a216-9131-431b-809c-f5a83146343d
Dewar-Haggart, Rachel
7ae70377-352a-4297-9798-a6aed0e1c04b
Naiseh, Mohammad
2c521cc6-0405-45f8-8d55-7d14eb4e4126
Underwood, Tim
8e81bf60-edd2-4b0e-8324-3068c95ea1c6
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Webb, Catherine, Thavanesan, Navamayooran, Dewar-Haggart, Rachel, Naiseh, Mohammad, Underwood, Tim and Vigneswaran, Ganesh
(2024)
Decision-making in oesophageal cancer MDTs: model vs clinician views of factor importance, and clinician perception of an ML tool.
Trustworthy Autonomous Systsems (TAS) Showcase, IEET, London, United Kingdom.
04 - 05 Mar 2024.
1 pp
.
Record type:
Conference or Workshop Item
(Poster)
Abstract
Treatment decisions for oesophageal cancer (OC) are multifaceted. A study combining a survey of clinicians with mixed methods and a comparison to a machine learning (ML) model was conducted to investigate the factors influencing these decisions. The study found that age and gender played a more significant role in the ML model than they did in the clinicians' conscious decision-making process. Additionally, we identified a variety of other important factors, such as specific tests and symptoms, social circumstances, and nutritional status. The prospect of utilising an ML-based decision support tool in the future received generally positive feedback, although opinions varied widely. Challenges to its adoption include perceptions of clinician superiority, the uniqueness of patient cases, the need for transparency and safeguards in the model, the requirements for data input, and the demand for proven effectiveness.
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Published date: March 2024
Venue - Dates:
Trustworthy Autonomous Systsems (TAS) Showcase, IEET, London, United Kingdom, 2024-03-04 - 2024-03-05
Identifiers
Local EPrints ID: 493230
URI: http://eprints.soton.ac.uk/id/eprint/493230
PURE UUID: f617afc9-d5b2-40f2-b38c-15747e04e81a
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Date deposited: 28 Aug 2024 16:52
Last modified: 29 Aug 2024 02:01
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Contributors
Author:
Catherine Webb
Author:
Navamayooran Thavanesan
Author:
Mohammad Naiseh
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