Case-based Reasoning for Predicting the Success of Therapy

Rosanne Janssen, P.H.M. Spronck, Arnoud Arntz

    Research output: Contribution to journalArticleScientificpeer-review

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    Abstract

    For patients with mental health problems, various treatments exist. Before a treatment is assigned to a patient, a team of clinicians must decide which of the available treatments has the best chance of succeeding. This is a difficult decision to make, as the effectiveness of a treatment might depend on various factors, such as the patient’s diagnosis, background and social environment. Which factors are the predictors for successful treatment is mostly unknown. In this article, we present a case-based reasoning approach for predicting the effect of treatments for patients with anxiety disorders. We investigated which techniques are suitable for implementing such a system to achieve a high level of accuracy. For our evaluation, we used data from a professional mental healthcare centre. Our application correctly predicted the success factor of 65% of the cases, which is significantly higher than the prediction of the baseline of 55%. Under the condition that the prediction was based on only cases with a similarity of at least 0.62, the success rate of 80% of the cases was predicted
    correctly. These results warrant further development of the system.
    Original languageEnglish
    Pages (from-to)165-177
    Number of pages13
    JournalExpert Systems: The Journal of Knowledge Engineering
    Volume32
    Issue number2
    DOIs
    Publication statusPublished - 2015

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    Case based reasoning
    Case-based Reasoning
    Therapy
    Medical problems
    Anxiety
    Prediction
    Healthcare
    Disorder
    Predictors
    Baseline
    Health
    Unknown
    Evaluation

    Cite this

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    title = "Case-based Reasoning for Predicting the Success of Therapy",
    abstract = "For patients with mental health problems, various treatments exist. Before a treatment is assigned to a patient, a team of clinicians must decide which of the available treatments has the best chance of succeeding. This is a difficult decision to make, as the effectiveness of a treatment might depend on various factors, such as the patient’s diagnosis, background and social environment. Which factors are the predictors for successful treatment is mostly unknown. In this article, we present a case-based reasoning approach for predicting the effect of treatments for patients with anxiety disorders. We investigated which techniques are suitable for implementing such a system to achieve a high level of accuracy. For our evaluation, we used data from a professional mental healthcare centre. Our application correctly predicted the success factor of 65{\%} of the cases, which is significantly higher than the prediction of the baseline of 55{\%}. Under the condition that the prediction was based on only cases with a similarity of at least 0.62, the success rate of 80{\%} of the cases was predictedcorrectly. These results warrant further development of the system.",
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    Case-based Reasoning for Predicting the Success of Therapy. / Janssen, Rosanne; Spronck, P.H.M.; Arntz, Arnoud.

    In: Expert Systems: The Journal of Knowledge Engineering, Vol. 32, No. 2, 2015, p. 165-177.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - Janssen, Rosanne

    AU - Spronck, P.H.M.

    AU - Arntz, Arnoud

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    AB - For patients with mental health problems, various treatments exist. Before a treatment is assigned to a patient, a team of clinicians must decide which of the available treatments has the best chance of succeeding. This is a difficult decision to make, as the effectiveness of a treatment might depend on various factors, such as the patient’s diagnosis, background and social environment. Which factors are the predictors for successful treatment is mostly unknown. In this article, we present a case-based reasoning approach for predicting the effect of treatments for patients with anxiety disorders. We investigated which techniques are suitable for implementing such a system to achieve a high level of accuracy. For our evaluation, we used data from a professional mental healthcare centre. Our application correctly predicted the success factor of 65% of the cases, which is significantly higher than the prediction of the baseline of 55%. Under the condition that the prediction was based on only cases with a similarity of at least 0.62, the success rate of 80% of the cases was predictedcorrectly. These results warrant further development of the system.

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