Using Anticipative Hybrid Extreme Rotation Forest to predict emergency service readmission risk

Artetxe, Arkaitz; Ayerdi, Borja; Graña, Manuel; Ríos, Sebastián A.

doi:10.1016/j.jocs.2016.12.008

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…It is unrealistic to apply the same risk assessment/prediction model in two countries with huge differences in life parameters and conditions. Therefore, it is widely recognized that predictive models need to be developed at each site using local data [1, 16]. Because hospital readmission is a much less frequent event than no readmission, data used in all reported studies is heavy class imbalance [17].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Despite this long history of studies about hospital readmission for adult patients, there are almost no studies devoted to readmission of pediatric patients [2]. In the pediatric case, hospital readmission prediction has been only reported in the setting of emergency department [16, 17] and intensive care units [18]. Few studies report results on both adult and pediatric patients [7], finding lower sensitivity in the pediatric population than in the adult population, due to greater class imbalance in the pediatric datasets.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Wolff

Graña

Ríos

et al. 2019

BioMed Research International

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Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Wolff

Graña

Ríos

et al. 2019

BioMed Research International

Self Cite

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“…The reported applications of deep learning to readmission prediction are restricted to a specific disease, that is, lupus patients (Reddy & Delen, ), for which there are long clinical histories per patient accessible through the EHR, so that the abundance of data allows for the training of deep models. Therefore, we focus on the following well‐known machine‐learning classification methods (Artetxe, Beristain, Graña and Besga ; Artetxe, Ayerdi, Graña & Rios ; Artetxe, Ayerdi, Graña, & Beristain ; Artetxe et al, ; Garmendia et al, ; Garmendia et al, ): •Linear discrimination analysis (LDA) and variants quadratic discriminant analysis (QDA), and mixture discriminant analysis (MDA) are the most standard linear models that provide a baseline result from linear discriminant theory, which is well grounded and accepted by the medical researchers. •Support vector machines (SVM) are the most standard machine‐learning algorithm in the biosciences literature used for predictive analysis, we explore both linear and non‐linear approaches, the later using the so‐called kernel trick. They have been tested extensively on readmission studies. •Multilayer perceptrons (MLP) are the classical artificial neural network approach to build nonlinear discriminant models.…”

Section: Methodsmentioning

confidence: 99%

Modelling hospital readmissions under frailty conditions for healthy aging

et al. 2019

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In the current context of an aging population in many developed countries, the issue of healthy aging is at the forefront of the political, scientific, and technological concerns. The frailty accompanying the late years of elderly people (>70 years old) deserves special consideration due to its great economical and personal costs and the workload imposed on the health care system. Hospital readmissions under a short time after hospital discharge are one of the sources of concern, and much effort is being devoted to their prediction for better care of the elder and optimized resource management. In this paper, we consider the prediction of readmissions for patients that are evaluated positively in the frailty scales. The computational experiments are carried out over a gender‐balanced cohort of 645 patients recruited at the University Hospital of Alava. We report machine‐learning prediction results of the readmission before the standard readmission limit of 30 days. We apply an upsampling technique to correct for class imbalance. Results are positive, encouraging further research and the creation of larger cohorts in international efforts.

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“…Artetxe et al conducted a susceptibility study on patient readmission based on the RF model. The prediction accuracy of this model is more than 70%, which indicates that the RF model can be used for on-site analysis of medical staff(Artetxe et al, 2017). Although the RF model is a relatively advanced algorithm, in order to further explore the advantages of the algorithm and meet the needs of scientific research, it is also an extensional application of the model to build integrated models by coupling with other algorithms.…”

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confidence: 99%

Comparison of rotation forest based intelligence models for landslide susceptibility modeling

Zhao¹,

Chen²,

Li³

et al. 2021

Preprint

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The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

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Using Anticipative Hybrid Extreme Rotation Forest to predict emergency service readmission risk

Cited by 12 publications

References 35 publications

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Modelling hospital readmissions under frailty conditions for healthy aging

Comparison of rotation forest based intelligence models for landslide susceptibility modeling

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