Towards graph-based class-imbalance learning for hospital readmission

Du, Guodong; Zhang, Jia; Ma, Fenglong; Zhao, Min; Lin, Yaojin; Li, Shaozi

doi:10.1016/j.eswa.2021.114791

Cited by 18 publications

(6 citation statements)

References 59 publications

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“…Wang et al [ 12 ] proposed a cost-sensitive deep learning model to address the imbalanced problem in medical data. In another study, Du et al [ 22 ] introduced a method combining a graph-based technique, an optimization framework, and a neural network to achieve superior performance in predicting hospital readmissions.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hospital Readmissions with Machine Learning

et al. 2022

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Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.

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Section: Introductionmentioning

confidence: 99%

Forecasting Hospital Readmissions with Machine Learning

et al. 2022

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“…The graph-based method, which creates a similar AI model concept used for graph or image recognition, was also proposed for designing models for readmission prediction. For example, in [48], the graph-based class-imbalance learning (graph-CL) method was adopted for constructing within-class graphs (for positive and negative samples) as well as a between-class graph for learning the pattern discrimination from within-class and between-class samples, and it reached a predictive performance of AUC = 0.776-0.886 for predicting readmission.…”

Section: Ai Models For Predicting Associated Events Of Hospital Admis...mentioning

confidence: 99%

“…In addition to transfer learning, other advanced AI methods, such as extreme gradient boosting (XGBoost) [37][38][39], time trajectory learning of adopted features [44], integrated AI models [45], feature selection algorithms [46,47], and graph-based methods [48] mentioned in Section 1.3, may also be adopted for model construction to further elevate the predictive performance of pneumonia readmission prediction.…”

Section: Future Workmentioning

confidence: 99%

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

Hsu

Lin

et al. 2022

Electronics

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A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

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“…In recent years, several readmission prediction models have been proposed for clinical decision support. [4][5][6][7][8] However, most of the prediction algorithms have limitations and poor prediction ability. Therefore, more sophisticated models need to be designed to evaluate the performance of patients after discharge.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the hospital readmission rate was proposed to identify patients who experience inferior care and compare quality among different hospitals. In recent years, several readmission prediction models have been proposed for clinical decision support 4–8 . However, most of the prediction algorithms have limitations and poor prediction ability.…”

Section: Introductionmentioning

confidence: 99%

Hospital readmission prediction with hybrid‐sampling and self‐paced balance learning

Xu,

Zhang,

Wang

et al. 2024

Concurrency and Computation

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SummaryHospital readmission prediction is defined as an evaluation task to model the historical medical data to predict whether patients will be readmitted after discharge. In the past few years, many feasible and effective prediction methods have been proposed, however, most of them neglect the imbalanced distribution of medical data, which causes great difficulties in modeling. Thus, we proposed a new hospital readmission prediction method, which utilizes hybrid‐sampling and self‐paced balance learning strategies to solve the class‐imbalance problem. To be specifically, we first employ an interference negative sample deletion strategy to reduce the probability of important majority class samples being deleted. Then, we design a hard positive sample generation strategy to generate more positive samples. Meanwhile, we also introduce a self‐paced balance factor during the oversampling process to improve the similarity between newly generated minority class samples and hard positive samples. Finally, we perform the experiments on six real‐world readmission datasets to indicate the superiority of the proposed method.

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Towards graph-based class-imbalance learning for hospital readmission

Cited by 18 publications

References 59 publications

Forecasting Hospital Readmissions with Machine Learning

Forecasting Hospital Readmissions with Machine Learning

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

Hospital readmission prediction with hybrid‐sampling and self‐paced balance learning

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