Forecasting Hospital Readmissions with Machine Learning

Michailidis, Panagiotis; Dimitriadou, Athanasia; Papadimitriou, Theophilos; Gogas, Periklis

doi:10.3390/healthcare10060981

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…In addition, the support vector machine, K-nearest neighbors, and multi-layer perceptron approaches all demonstrated admirable performance. Therefore, the results obtained in this study are consistent and reliable, which is supported by the fact that they align well with the study conducted in 2022 by Michailidis and colleagues [ 28 ]. This implies that the effectiveness of these classifiers can be, at least in part, generalized and is not limited to a particular dataset or context.…”

Section: Discussionsupporting

confidence: 92%

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

Rizinde,

Ngaruye,

Cahill

2023

JPM

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High rates of hospital readmission and the cost of treating heart failure (HF) are significant public health issues globally and in Rwanda. Using machine learning (ML) to predict which patients are at high risk for HF hospital readmission 20 days after their discharge has the potential to improve HF management by enabling early interventions and individualized treatment approaches. In this paper, we compared six different ML models for this task, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), logistic regression (LR), decision trees (DT), random forests (RF), and support vector machines (SVM) with both linear and radial basis kernels. The outputs of the classifiers are compared using performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We found that RF outperforms all the remaining models with an AUC of 94% while SVM, MLP, and KNN all yield 88% AUC. In contrast, DT performs poorly, with an AUC value of 57%. Hence, hospitals in Rwanda can benefit from using the RF classifier to determine which HF patients are at high risk of hospital readmission.

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Section: Discussionsupporting

confidence: 92%

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

Rizinde,

Ngaruye,

Cahill

2023

JPM

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“…The study found that the predictive model had a high degree of accuracy and improved resource allocation. In a study by Michailidis et al (2022), machine learning used to predict the likelihood of patient readmissions [29]. The study found that the predictive model had a high degree of accuracy and o identified patients who were at high risk for readmission, enabling early intervention to prevent complications.…”

Section: Literature Surveymentioning

confidence: 99%

Data-Driven Approaches to Hospital Capacity Planning and Management

2023

infkm

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Data-driven approaches to hospital capacity planning and management involve using historical and real-time data to identify patterns and trends in patient demand, resource utilization, and other key metrics. This information provides to develop predictive models, forecast patient demand, optimize staffing levels, and improve patient outcomes. Electronic health record systems and Internet of Things devices can also be used to monitor hospital operations in real-time and identify areas of inefficiency. Hospital capacity planning and management are critical to ensuring that healthcare facilities have enough resources to meet the needs of their patients. Data-driven approaches can be helpful in addressing these challenges by providing insights into patient demand, resource utilization, and other key metrics. This article discusses the various data-driven approaches to hospital capacity planning and management and their potential benefits. It also highlights the importance of having the right infrastructure and expertise effectively collect, analyse, and act on this data.

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“…In healthcare, the RF has shown promising results in various applications, such as predicting the severity of patient falls [19] and forecasting hospital readmissions [20].…”

Section: Introductionmentioning

confidence: 99%

Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency

Maria Concepcion Suarez Vera

2024

Adv. technol. innov.

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This study investigates the precision geolocation of medicinal plants, a critical endeavor bridging ecology, conservation, and pharmaceutical research. By employing machine learning algorithms—gradient boosting machine (GBM), random forest (RF), and support vector machine (SVM)—within the cross-industry standard process for data mining (CRISP-DM) framework, both the accuracy and efficiency of medicinal plant geolocation are enhanced. The assessment employs precision, recall, accuracy, and F1 score performance metrics. Results reveal that SVM and GBM algorithms exhibit superior performance, achieving an accuracy of 97.29%, with SVM showing remarkable computational efficiency. Meanwhile, despite inferior performance, RF remains competitive especially when model interpretability is required. These outcomes highlight the efficacy of SVM and GBM in medicinal plant geolocation and accentuate their potential to advance environmental research, conservation strategies, and pharmaceutical explorations. The study underscores the interdisciplinary significance of accurately geolocating medicinal plants, supporting their conservation for future pharmaceutical innovation and ecological sustainability.

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Forecasting Hospital Readmissions with Machine Learning

Cited by 11 publications

References 25 publications

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

Data-Driven Approaches to Hospital Capacity Planning and Management

Precision Geolocation of Medicinal Plants: Assessing Machine Learning Algorithms for Accuracy and Efficiency

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