Predictive modeling of hospital readmissions using metaheuristics and data mining

Zheng, Bichen; Zhang, Jinghe; Yoon, Sang Won; Lam, Sarah S.; Khasawneh, Mohammad T.; Poranki, Srikanth

doi:10.1016/j.eswa.2015.04.066

Cited by 108 publications

(54 citation statements)

References 45 publications

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“…A BP neural network consists of an input layer, at least one hidden layer and an output layer [13]. The training of BP neural network involves a forward process and a backward process [14]. And in the feed-forward process, the signals are calculated through network, and the errors are computed at the output layer.…”

Section: Methodsmentioning

confidence: 99%

International carbon market price forecasting using an integration model based on SVR

Jiang¹,

Wu²

2015

Proceedings of the 2015 International Conference on Engineering Management, Engineering Education and Information Technology

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Abstract. Better forecast of carbon emission prices may increase risk control capabilities of emission market stakeholders, and provide decision support for policy makers, financial institutions and enterprises. However, the issue of carbon price forecasting is complicated and several existing prediction models are difficult to achieve satisfactory results. This paper proposes an integration model based on SVR to predict international carbon market price. The model we suggest includes two steps: we respectively establish ARIMA, BP neural network, grey model GM(1,1) and genetic programming to fit the original sequence of the carbon price at the first phase, and get four prediction results. Additionally, SVR is used to integrate these four results and eventually obtain prediction result. For verification and testing, EUA (DEC15) carbon price from December 3, 2012 to April 10, 2015 under the EU ETS was used to examine the forecasting ability of the proposed integration model. The result demonstrates the accuracy of integration model proposed in this paper is superior to the other three basic models.

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Section: Methodsmentioning

confidence: 99%

International carbon market price forecasting using an integration model based on SVR

Jiang¹,

Wu²

2015

Proceedings of the 2015 International Conference on Engineering Management, Engineering Education and Information Technology

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“…7 This is because the availability of large amounts of medical data has the potential to be used in new ways to gain insights on how to improve healthcare outcomes. [9][10][11][12][13][14][15] Lastly, most ML studies have used area under the receiver operating characteristic curve (AUC) as the only performance metric to assess the model performance. 8 Previous studies have complemented administrative data with clinical information to develop predictive models.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics

et al. 2019

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Aims Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. Methods and results We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi‐layer perceptron (MLP)‐based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP‐based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity. Conclusions We show that for the medical data with class imbalance, the proposed MLP‐based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.

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“…Several researchers have focused on patient classification or prioritization . Zheng et al proposed a range of data mining approaches, including neural networks, random forest (RF) algorithms, a hybrid model of swarm intelligence, and support vector machines (SVM) to classify high‐ and low‐risk readmitted patients. Pollettini et al developed a computer‐aided approach for the automatic recommendation of surveillance levels based on a linguistic module and machine‐learning classification modules using information from electronic patient records.…”

Section: Introductionmentioning

confidence: 99%

Using machine‐learning methods to support health‐care professionals in making admission decisions

Luo

Shen

2019

Health Planning & Management

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Summary Background Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health‐care professional ranking patients by analyzing registration information. Objective The purpose of this study was to develop a machine‐learning approach to screening, using historical data and the experience of health‐care professionals to develop a set of screening rules to help health‐care professionals prioritize patient needs automatically. Methods We used five machine‐learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient‐boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. Results The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. Conclusion The results reported here indicate that machine‐learning techniques can be valuable for automating the screening process. Our model can assist health‐care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.

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Predictive modeling of hospital readmissions using metaheuristics and data mining

Cited by 108 publications

References 45 publications

International carbon market price forecasting using an integration model based on SVR

International carbon market price forecasting using an integration model based on SVR

Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics

Using machine‐learning methods to support health‐care professionals in making admission decisions

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