Abstract
An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.
Highlights
Hospital beds are one of the important medical resources, and these beds are usually used as an important indicator to measure the hospital service level, which can objectively reflect the development level of local medical system [1]
Coskun et al [13] used the Markov process to analyze the hospitalization process of patient, which is divided into short, medium, and long hospital stays. e PH distribution is used to fit the distribution of the length of stay, and the maximum likelihood estimation method is used to obtain the estimated value of the parameter. e study pointed out the inadequacy of choosing Journal of Healthcare Engineering lognormal distribution and gamma distribution to fit the length of hospital stay. e empirical analysis results show that the use of the 6-phase Markov model to fit the length of stay is better than other distribution, but there is overfitting phenomenon
A novel improved model based on the classical maximum likelihood estimation and the EM algorithm is used to fit a group of real data in the hospital, where the results show that the effect of the proposed model is good
Summary
Hospital beds are one of the important medical resources, and these beds are usually used as an important indicator to measure the hospital service level, which can objectively reflect the development level of local medical system [1]. Its prediction is to use statistical methods to summarize, analyze, and study its change rule and its distribution law and use machine learning algorithms [5, 6] to build models to predict the length of hospital stay [7] Are these important key technology that need to be broken through in theoretical research, but they have a certain engineering value for hospital bed scheduling arrangements and the improvement of hospital rescue capabilities [8]. In the case of asymmetric data distribution of patient length of stay, the decisions made by hospital managers based on the average length of stay may lead to unreasonable allocation of hospital beds and unnecessary losses. By constructing the prediction model of inpatient length of stay, this paper discusses the application of the improved algorithm in the prediction of inpatient length of stay, hoping to bring some help to hospital managers in the scheduling and arrangement of hospital beds
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