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A comorbidity-based recommendation engine for disease prediction

A comorbidity-based recommendation engine for disease prediction,10.1109/CBMS.2010.6042664,Francesco Folino,Clara Pizzuti

A comorbidity-based recommendation engine for disease prediction   (Citations: 1)
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A recommendation engine for disease prediction that combines clustering and association analysis techniques is proposed. The system produces local prediction models, specialized on subgroups of similar patients by using the past patient medical history, to determine the set of possible illnesses an individual could develop. Each model is generated by using the set of frequent diseases that contemporarily appear in the same patient. The illnesses a patient could likely be affected in the future are obtained by considering the items induced by high confidence rules generated by the frequent diseases. Experimental results show that the proposed approach is a feasible way to diagnose diseases.
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    • ...The prediction of illnesses based on the past patient medical history revealed efficacious in foreseeing diseases a patient could likely be affected in the future [5, 4, 8, 7, 18]...
    • ...In [7] a system, named CORE, that combines clustering and association analysis on the data set of patient records to generate local specialized and accurate prediction models has been presented...
    • ...In this paper we propose an extension of the approach presented in [7], named...
    • ...Grouping the set T of patients in k groups having similar disease history is an effective way of improving the accuracy of the prediction model, as showed in [7]...
    • ...In the case the Markov model is unable to provide us with a reliable prediction, the approach proposed in [7] is used instead to circumvent the problem...
    • ...In this case of uncertainty, in order to disambiguate the choice, we resort to the predictive capability of the association rules in the same way we proposed in [7, 8]. By fixing σ =0 .5, the frequent itemsets for C1 are shown in Table 4...
    • ...After that, for the sake of comparison, we want to show that the overall prediction performances of CORE + are better than those obtained by the approach CORE [7] . In order to perform a fair comparison, we recur to a well-known metric, the F-measure [19], which is the harmonic mean between precision and recall, and it is often used to examine the tradeoff between them:...

    Francesco Folinoet al. Combining Markov Models and Association Analysis for Disease Predictio...

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