Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Abdurrab, Ibrahim; Mahmood, Tariq; Sheikh, Sana; Aijaz, Saba; Kashif, Muhammad; Memon, Ahson; Ali, Imran; Peerwani, Ghazal; Pathan, Asad; Alkhodre, Ahmad B.; Siddiqui, Muhammad Shoaib

doi:10.3390/healthcare12020249

Cited by 4 publications

(3 citation statements)

References 68 publications

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“…The RF model exhibited superior performance compared to the other models, with an accuracy of 0.80%. In one of the most recent works of research, Abdurrab et al [ 17 ] evaluated Bayesian regression models and ML regression models for predicting the LOS of cardiac patients undergoing cardiac bypass surgery at Tabba Heart Institute, Karachi, Pakistan, between 2015 and 2020. Hierarchical Bayesian regression outperforms simple Bayesian regression and ML models in accuracy (RMSE = 1.49, MAE = 1.16) and interpretability, particularly in handling data variability and extreme values without removing outliers.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of LOS prediction, the existing literature employed ML models including Support Vector Machine (SVM), Decision Tree (DT), artificial neural network (ANN), Bayesian Network, Convolutional Neural Network (CNN) and ensemble learning [ 14 , 15 , 16 , 17 ] for diverse range of datasets (i.e., King Abdul Aziz Cardiac dataset [ 4 ], Microsoft dataset [ 18 ], New York Hospitals dataset [ 19 ], United States Hospital dataset [ 20 ], Royal Adelaide Hospital dataset [ 21 ]), demonstrating significant potential of ML techniques towards addressing the problem. However, most of the existing literature treats LOS prediction as a classification problem, overlooking the nuanced details of LOS duration.…”

Section: Introductionmentioning

confidence: 99%

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Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care

AlMuhaideb,

bin Shawyah,

Alhamid

et al. 2024

Healthcare

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Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care

AlMuhaideb,

bin Shawyah,

Alhamid

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…This limits the ability to determine the important prediction insights of each hospital stay duration, showing a significant research gap. Therefore, to address this gap, our study focuses on four distinct LOS classes: short, medium, long, and extended stays (Abdurrab et al, 2024;Junior et al, 2024;Momo et al, 2024). A short stay typically signifies quick recoveries, though it may sometimes indicate early mortality.…”

Section: Introductionmentioning

confidence: 99%

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Miriyala,

Sinha

2024

Front. Artif. Intell.

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Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model—a Particle Swarm Optimized-Enhanced NeuroBoost—that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimensionality of the data is reduced using deep neural autoencoders. The reconstructed data obtained from autoencoders is given as input to an eXtreme gradient boosting model. Finally, the model is tuned with particle swarm optimization to obtain optimal hyperparameters. With the proposed technique, the model achieved superior performance with an overall accuracy of 98.8% compared to traditional ensemble models and past research works. The model also scored highest in other metrics such as precision, recall, and particularly F1 scores for all categories of hospital stay. These scores validate the suitability of our proposed model in medical healthcare applications.

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Artificial intelligence in cardiac surgery: A systematic review

Sulague,

Beloy,

Medina

et al. 2024

World j. surg.

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BackgroundArtificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.MethodsA systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.ResultsA total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.ConclusionAI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision‐making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.

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Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Cited by 4 publications

References 68 publications

Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care

Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Artificial intelligence in cardiac surgery: A systematic review

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