Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Lu, Xiwen; Qiu, Hang

doi:10.1186/s12911-023-02159-7

Cited by 5 publications

(10 citation statements)

References 40 publications

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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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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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“…Along similar lines, Lu and Qiu have focused on forecasting daily hospital admissions due to cerebrovascular disease and claimed that their approach offers practical value for hospital management teams in early warning and healthcare resource allocation. 63 In Usmani et al 64 prediction of the trends of daily and monthly hospitalization has been conducted and along similar lines Jalili et al 65 forecast the number of hospital admissions of CVD patients without however elaborating on a specific application context. Hu et al 66 identified major determinants of stroke incidence at the neighborhood level potentially useful to prioritize and allocate resources to optimize community-level interventions for stroke prevention.…”

Section: Research Questionsmentioning

confidence: 99%

“…Proceeding with the remaining 30% of pertinent literature that does not conduct performance comparison whatsoever, a part of research opted for a non-linear algorithm, 42,43,56,57 while another part has focused on an ensemble algorithm of their selection. 50,51,58,59 As it becomes obvious from Figure 4 ensemble algorithms are the most popular choice encountered in pertinent literature, 35,[39][40][41][44][45][46][47][48][49][50][51][52][53][54][55]58,[60][61][62][63][66][67][68] closely followed by non-linear algorithms. 35,[41][42][43][44][45][46][47][48][49]54,59,60,62,[64]…”

Section: Research Questionsmentioning

confidence: 99%

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Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

Argyri,

Gallos,

Amditis

et al. 2024

Preprint

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Cardiovascular disease has been established as the world’s number one killer, causing over 20 million deaths per year. This fact, along with the growing awareness of the impact of exposomic risk factors on cardiovascular diseases, has led the scientific community to leverage machine learning strategies as a complementary approach to traditional statistical epidemiological studies that are challenged by the highly heterogeneous and dynamic nature of exposomics data. The principal objective served by this work is to identify key pertinent literature and provide an overview of the breadth of research in the field of machine learning applications on exposomics data with a focus on cardiovascular diseases. Secondarily, we aimed at identifying common limitations and meaningful directives to be addressed in the future. Overall, this work shows that, despite the fact that machine learning on exposomics data is under-researched compared to its application on other members of the -omics family, it is increasingly adopted to investigate different aspects of cardiovascular diseases.

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“…Hence, selecting the appropriate combination of primary learners for the stacking ensemble model is of utmost importance. [38] Currently existing methods for selecting primary learners to build ensemble models can be categorized into three main approaches: those based on manual selection guided by human expertise, [39][40][41] those based on the performance of candidate models, and those based on the diversity of model combinations. [42] Fang et al proposed a combined model [43] that integrates k-nearest neighbours (KNN) and light gradient boosting machine (Light GBM) to address the risk prediction of hypertension over the next 5 years.…”

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confidence: 99%

Soft sensor based on multi‐phase stacking ensemble model with self‐selected primary learner for batch processes

Zhao,

Yang,

et al. 2023

Can J Chem Eng

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In batch processes, existing soft sensing methodologies encounter substantial challenges when confronted with nonlinearity and multi‐phase issues. In response to these challenges, an innovative soft sensing framework known as the multi‐phase stacking ensemble model with self‐selected primary learner is proposed. The main innovation of this framework lies in the solution to the primary learner selection issue within the stacking model. To commence, the batch process is divided into multiple phases employing a Gaussian mixture model, thereby establishing local ensemble models for each phase. Subsequently, the iterative self‐selection of primary learners strategy is proposed, which iteratively selects suitable primary learners for these models, optimizing their combination of primary learners for each local model. This primary learner selection strategy effectively enhances the accuracy of predictions in the stacking ensemble model. To further enhance the performance, Bayesian optimization is utilized to tune the hyperparameters of each local ensemble model. This step guarantees optimal performance of the model across diverse phases. Extensive simulation experiments are conducted on an industrial penicillin fermentation process to validate the effectiveness of the proposed framework. According to the findings, the model demonstrated superior performance compared to existing single‐learner soft sensing methods and commonly utilized ensemble‐based soft sensing methods in terms of both R2 score (0.97584) and RMSE (0.0513). Overall, this framework offers a novel approach for selecting primary learners in stacking ensemble models and enhancing the predictive performance in batch processes for soft sensing.

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Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Cited by 5 publications

References 40 publications

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

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

Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

Soft sensor based on multi‐phase stacking ensemble model with self‐selected primary learner for batch processes

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