An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

Zhu, Xiuqing; Hu, Jinqing; Xiao, Tao; Huang, Shanqing; Wen, Yuguan; Shang, Dewei

doi:10.3389/fphar.2022.975855

Cited by 8 publications

(7 citation statements)

References 60 publications

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“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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“…However, the stack-based ensemble technique introduces an additional layer of complexity to the model, potentially making its decision-making process less transparent and comprehensible [13]. Researchers are actively exploring methods to enhance the explainability of stacking models and to make them transparent for real-world applications [51][52][53][54]. In our study, the feature permutation technique was used for the RF model [31].…”

Section: Discussionmentioning

confidence: 99%

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Kuo,

Chen,

et al. 2024

Eur Radiol Exp

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Background This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction. Graphical Abstract

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“…Stage 2: Min-max normalization of continuous predictors. The stability and prediction performance of ML algorithms depend on the quality of input data [33], [60]. Observations of all continuous predictor variables (except for dummy variables) were rewritten in the [0, 1] interval to eliminate scale effects, using the expression:…”

Section: Stepmentioning

confidence: 99%

Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

Porto,

Fogliatto

2024

Preprint

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Background Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of patient arrivals in EDs can serve as a management baseline for better allocation of staff and medical resources. In this article, we investigate the use of calendar and meteorological predictors, as well as feature engineered variables, to forecast daily patient arrivals using datasets from eleven different EDs across 3 countries. Methods Six machine learning algorithms were tested, considering forecasting horizons of 7 and 45 days ahead. Tuning of hyperparameters was performed using a grid-search with cross-validation. Algorithms' performance was evaluated using 5-fold cross-validation and four performance metrics. Results The eXtreme Gradient Boosting (XGBoost) achieved better performance considering the two prediction horizons compared to other models, also outperforming results reported in past studies on ED arrival prediction. This is also the first study to utilize Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF) and Neural Network Autoregression (NNAR) for predicting patient arrivals at EDs. Conclusion The Random Forest (RF) variable selection and grid-search methods improved the accuracy of the algorithms tested. Our study innovates by using feature engineering to predict patient arrivals in EDs.

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An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

Cited by 8 publications

References 60 publications

Machine Learning: A New Approach for Dose Individualization

Machine Learning: A New Approach for Dose Individualization

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

Enhancing emergency department patient arrival forecasting: a study using feature engineering and advanced machine learning algorithms

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