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EP-1224: An Australian radiotherapy decision support system with contextual justification

Journal Article


Abstract


  • Background: There is great potential to utilise a large range

    of retrospective clinical data as an evidence base in decision

    support systems (DSS) for cancer prognosis and subsequent

    personalised treatment decisions. Recently, there were

    several DSSs built for this purpose using machine learning

    tools, mainly regression models, Bayesian Networks (BN) and

    Support Vector Machines (SVM). These machine learning tools

    provide only a prediction of a class (decision), based on input

    attributes that were used to build the model, without

    providing additional information to clinicians about how and

    why this prediction was made.

    Objective: To investigate the performance of an alternative

    machine learning tool in building a lung cancer radiotherapy

    DSS that provides clinicians with an estimated prediction

    together with the influencing attributes and their values

    (evidence) in supporting the decision reached. This will

    provide contextual justification to clinicians regarding the

    decisions, which will further help them in deciding whether

    to adopt the machine prediction or not.

Publication Date


  • 2016

Citation


  • M. Barakat, M. Field, D. Stirling, L. Holloway, A. Ghose, M. Bailey, M. Carolan, A. Dekker, G. Delaney, G. Goozee, et al "EP-1224: An Australian radiotherapy decision support system with contextual justification," Radiotherapy and Oncology, vol. 119, (Supplement 1) pp. S580-S580, 2016.

Start Page


  • S580

End Page


  • S580

Volume


  • 119

Issue


  • Supplement 1

Abstract


  • Background: There is great potential to utilise a large range

    of retrospective clinical data as an evidence base in decision

    support systems (DSS) for cancer prognosis and subsequent

    personalised treatment decisions. Recently, there were

    several DSSs built for this purpose using machine learning

    tools, mainly regression models, Bayesian Networks (BN) and

    Support Vector Machines (SVM). These machine learning tools

    provide only a prediction of a class (decision), based on input

    attributes that were used to build the model, without

    providing additional information to clinicians about how and

    why this prediction was made.

    Objective: To investigate the performance of an alternative

    machine learning tool in building a lung cancer radiotherapy

    DSS that provides clinicians with an estimated prediction

    together with the influencing attributes and their values

    (evidence) in supporting the decision reached. This will

    provide contextual justification to clinicians regarding the

    decisions, which will further help them in deciding whether

    to adopt the machine prediction or not.

Publication Date


  • 2016

Citation


  • M. Barakat, M. Field, D. Stirling, L. Holloway, A. Ghose, M. Bailey, M. Carolan, A. Dekker, G. Delaney, G. Goozee, et al "EP-1224: An Australian radiotherapy decision support system with contextual justification," Radiotherapy and Oncology, vol. 119, (Supplement 1) pp. S580-S580, 2016.

Start Page


  • S580

End Page


  • S580

Volume


  • 119

Issue


  • Supplement 1