Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

Wu, Cuiyun; Zha, Dahui; Gao, Hong

doi:10.1155/2022/9275801

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Due to the skewed distribution of hospitalization costs for colorectal cancer patients, traditional linear regression models have limitations in the study of factors influencing hospitalization costs. The studies have shown that ( 14 , 22 ) RF algorithm and SVR algorithm are more suitable for the prediction of hospital costs as compared to other algorithms. And no study has compared RF algorithms to SVR algorithms in the prediction of the cost of hospitalization.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the RF algorithm and SVR algorithm were selected in this study for modeling and comparative. Some research shows that machine learning approaches are more applicable to the study of big data in healthcare, and other studies have shown that SVR models have a strong generalization ability in hospitalization cost prediction compared to other machine learning algorithms ( 22 ). Therefore, this study introduces support vector regression(SVR) models to explore the factors influencing hospitalization costs.…”

Section: Methodsmentioning

confidence: 99%

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Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study

Gao,

Liu

2024

Front. Public Health

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AimsAs people’s standard of living improves, the incidence of colorectal cancer is increasing, and colorectal cancer hospitalization costs are relatively high. Therefore, predicting the cost of hospitalization for colorectal cancer patients can provide guidance for controlling healthcare costs and for the development of related policies.MethodsThis study used the first page of medical record data on colorectal cancer inpatient cases of a tertiary first-class hospital in Shenzhen from 2018 to 2022. The impacting factors of hospitalization costs for colorectal cancer were analyzed. Random forest and support vector regression models were used to establish predictive models of the cost of hospitalization for colorectal cancer patients and to compare and evaluate.ResultsIn colorectal cancer inpatients, major procedures, length of stay, level of procedure, Charlson comorbidity index, age, and medical payment method were the important influencing factors. In terms of the test set, the R2 of the Random forest model was 0.833, the R2 of the Support vector regression model was 0.824; the root mean square error (RMSE) of the Random forest model was 0.029, and the RMSE of the Support vector regression model was 0.032. In the Random Forest model, the weight of the major procedure was the highest (0.286).ConclusionMajor procedures and length of stay have the greatest impacts on hospital costs for colorectal cancer patients. The random forest model is a better method to predict the hospitalization costs for colorectal cancer patients than the support vector regression.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study

Gao,

Liu

2024

Front. Public Health

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“…It also effectively avoids the shortcomings of traditional statistical theory that quickly make the model fall into the local minimum due to overfitting and too many dimensions. Its progressive nature makes statistical theory develop rapidly under the efforts of many researchers [ 14 , 15 ].…”

Section: The Concept Of Support Vector Machinementioning

confidence: 99%

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

Chen

2022

Computational Intelligence and Neuroscience

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Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was used to establish a model for predicting the drilling fluid performance parameters. This predictive model was used as part of the overall drilling fluid formulation optimization design model. For a given drilling fluid performance parameter requirement, this model can be applied to reverse the addition of various treatment agents, and finally, the prediction accuracy of the model is verified by experiments.

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Identify hospitalization cost drivers of traumatic fracture patients in China using quantile regression and backpropagation neural network

Liu

Hong

et al. 2023

Preprint

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Objective Analyze the factors associated with hospitalization costs of traumatic fracture patients.Methods Data for the retrospective analysis was extracted from the first pages of inpatient medical records in Zhuhai, China. The sample consisted of 31503 patients hospitalized for traumatic fractures between January 1, 2018 and December 31, 2020. We first compared differences in hospitalization costs between subgroups, followed by quantile regression and backpropagation neural network to investigate the key drivers of the hospitalization costs.Results The median hospitalization cost for traumatic fracture patients was ¥13528.2. The mean length of stay was 13.77 days. Quantile regression showed that higher hospitalization costs from the Quantile 0.1 to the Quantile 0.9 significantly correlated with advanced age, more severe types of fracture, operation, comorbidity, longer length of stay, higher level of hospital, and payment with Medicare. Backpropagation neural network indicated that the length of stay, operation level and hospital level were the most important predictors of hospitalization costs.Conclusion Quantile regression and backpropagation neural network yielded valuable information on the factors affecting the hospitalization costs of traumatic fractures in China. Findings suggested that interventions aiming to reduce length of stay contributed to reducing the economic burden associated with traumatic fractures.

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Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

Cited by 3 publications

References 31 publications

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

Identify hospitalization cost drivers of traumatic fracture patients in China using quantile regression and backpropagation neural network

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