يعرض 1 - 9 نتائج من 9 نتيجة بحث عن '"various mainstream models"', وقت الاستعلام: 0.37s تنقيح النتائج
  1. 1
    Academic Journal
    Supplementary Tables: S1 Table Feature importance scores as calculated by Functional ANOVA; S2 Table Complete list of variables and parameters included in the machine learning model.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/journal_contribution/Supplementary_Tables_S1_Table_Feature_importance_scores_as_calculated_by_Functional_ANOVA_S2_Table_Complete_list_of_variables_and_parameters_included_in_the_machine_learning_model_/24568527

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.s001
    https://figshare.com/articles/journal_contribution/Supplementary_Tables_S1_Table_Feature_importance_scores_as_calculated_by_Functional_ANOVA_S2_Table_Complete_list_of_variables_and_parameters_included_in_the_machine_learning_model_/24568527

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  2. 2
    Accuracy measures of the machine learning model prediction of major adverse cardiovascular events.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/dataset/Accuracy_measures_of_the_machine_learning_model_prediction_of_major_adverse_cardiovascular_events_/24568551

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.t004
    https://figshare.com/articles/dataset/Accuracy_measures_of_the_machine_learning_model_prediction_of_major_adverse_cardiovascular_events_/24568551

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  3. 3
    Image
    LIME explanation for a correctly predicted patient with a low risk of MACE.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/figure/LIME_explanation_for_a_correctly_predicted_patient_with_a_low_risk_of_MACE_/24568536

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.g003
    https://figshare.com/articles/figure/LIME_explanation_for_a_correctly_predicted_patient_with_a_low_risk_of_MACE_/24568536

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  4. 4
    Image
    LIME explanation for a correctly predicted patient with a high risk of MACE.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/figure/LIME_explanation_for_a_correctly_predicted_patient_with_a_high_risk_of_MACE_/24568539

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.g004
    https://figshare.com/articles/figure/LIME_explanation_for_a_correctly_predicted_patient_with_a_high_risk_of_MACE_/24568539

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  5. 5
    Image
    Permutation feature importance scores for the top ten features in the ensemble model for predicting MACE risk.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/figure/Permutation_feature_importance_scores_for_the_top_ten_features_in_the_ensemble_model_for_predicting_MACE_risk_/24568533

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.g002
    https://figshare.com/articles/figure/Permutation_feature_importance_scores_for_the_top_ten_features_in_the_ensemble_model_for_predicting_MACE_risk_/24568533

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  6. 6
    Image
    Feature importance ranking of the machine learning model for predictive MACE. Only the top 10 most important features were labelled.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/figure/Feature_importance_ranking_of_the_machine_learning_model_for_predictive_MACE_Only_the_top_10_most_important_features_were_labelled_/24568530

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.g001
    https://figshare.com/articles/figure/Feature_importance_ranking_of_the_machine_learning_model_for_predictive_MACE_Only_the_top_10_most_important_features_were_labelled_/24568530

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  7. 7
    Clinical outcomes of the patients included in this study.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/dataset/Clinical_outcomes_of_the_patients_included_in_this_study_/24568548

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.t003
    https://figshare.com/articles/dataset/Clinical_outcomes_of_the_patients_included_in_this_study_/24568548

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  8. 8
    Demographics and baseline characteristics of the enrolled participants.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/dataset/Demographics_and_baseline_characteristics_of_the_enrolled_participants_/24568542

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.t001
    https://figshare.com/articles/dataset/Demographics_and_baseline_characteristics_of_the_enrolled_participants_/24568542

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  9. 9
    Findings on the coronary computed tomography angiography (CCTA) and single-photon emission computed tomography (SPECT) imaging.

    المؤلفون: Fares Alahdab, Radwa El Shawi, Ahmed Ibrahim Ahmed, Yushui Han, Mouaz Al-Mallah

    مصطلحات موضوعية: Medicine, Genetics, Biotechnology, Immunology, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, various mainstream models, segment involvement score, second obtuse marginal, past medical history, mean age 61, left circumflex artery, invasive cardiovascular imaging, coronary ct angiography, automated machine learning, 61 %, available clinical data, 1 &# 177, accurately predict risk, spect myocardial imaging, including spect mpi, 54 %, respectively, 2 clinical features, +ml%22">xlink "> ml, explainable ml approach, %22">xlink ">

    Time: 99

    Relation: https://figshare.com/articles/dataset/Findings_on_the_coronary_computed_tomography_angiography_CCTA_and_single-photon_emission_computed_tomography_SPECT_imaging_/24568545

    الاتاحة: https://doi.org/10.1371/journal.pone.0291451.t002
    https://figshare.com/articles/dataset/Findings_on_the_coronary_computed_tomography_angiography_CCTA_and_single-photon_emission_computed_tomography_SPECT_imaging_/24568545

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