Population exacerbation incidence contains predictive information of acute exacerbations in patients with chronic obstructive pulmonary disease in telecare

Kronborg, Thomas; Mark, Lasse; Cichosz, Simon Lebech; Secher, Pernille Heyckendorff; Hejlesen, Ole

doi:10.1016/j.ijmedinf.2017.12.026

Cited by 12 publications

(1 citation statement)

References 21 publications

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“…Models that were heterogeneously ensemble by Voting generally performed well (over half of the heterogeneously integrated models were in the top 10 of all model units). And the optimal model obtained in this paper outperforms the prediction performance of some of previous studies [14,56,57], indicating that the joint ensemble framework is feasible to some degree. Furthermore, in this study, we innovatively use the comprehensive sorting and summing method to assign scores to all models, and nally evaluate the overall performance of the models by the combined scores, which provides an evaluation mode for selecting the model with the best overall performance according to the focus of the research area.…”

Section: Discussionmentioning

confidence: 56%

A Joint Ensemble Framework for the Detection of Acute Exacerbations in Chronic Obstructive Pulmonary Disease

Wang,

Zhang,

Cui

et al. 2023

Preprint

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Objective The purpose of this study was to create a joint ensemble framework for identifying AECOPD and providing a plausible explanation of model predictions. Methods From MIMIC-III, we extracted and organized records for COPD and AECOPD patients. Furthermore, we integrated missing value imputation, joint feature selection, advanced ML algorithms, Bayesian optimization techniques, and the SHAP interpretable method to construct a joint optimized ensemble framework, serving as the predictive model for AECOPD risk identification. The efficacy of the model's prediction was evaluated using a composite score of six evaluation measures. Results CAD and 19 other variables significantly impacted AECOPD. Various resampling methods and classifiers yielded diverse prediction accuracies. LightGBM and LR models with NC processing showcased optimal combined performance pre-heterogeneous combination. The Voting ensemble with MWMOTE achieved superior balanced classification. Conclusion The joint ensemble framework improved AECOPD risk identification performance in clinically relevant data of COPD patients admitted in the ICU.

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Section: Discussionmentioning

confidence: 56%