Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning
Shiyu Fan1, Abudoukeyoumujiang Abulizi2,3, Yi You4
1Department of Preventive Healthcare, Shihezi University, Shihezi, 832000, China.
BMC Infectious Diseases
|August 28, 2024
Summary
Machine learning models, particularly MLP, can accurately predict pulmonary tuberculosis hospitalization costs. This aids in optimizing resource allocation and managing patient expenses effectively.
Area of Science:
- Medical Informatics
- Health Economics
- Machine Learning in Healthcare
Background:
- Pulmonary tuberculosis (PTB) presents a significant economic burden on patients.
- Predicting hospitalization costs is crucial for effective resource allocation and cost management in PTB care.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting various hospitalization costs associated with PTB.
- To identify key factors influencing PTB hospitalization expenses.
Main Methods:
- Analysis of data from 9570 PTB patients (2020-2022).
- Application of multiple regression, Random Forest Regression (RFR), and Multilayer Perceptron (MLP) models.
- Prediction of seven cost categories: diagnostic, medical service, material, treatment, drug, other, and total hospitalization costs.
Main Results:
- Median total hospitalization cost was 13,150.45 yuan.
- Nine factors including age, length of stay, and drug resistance significantly impacted costs.
- MLP generally outperformed RFR and multiple regression in cost prediction accuracy.
Conclusions:
- MLP models effectively predict PTB hospitalization costs, enabling better cost structure rationalization.
- These predictive insights can inform resource allocation and potentially be applied to other medical conditions.
Keywords:
Cost predictionInfluencing factorsMachine learningMultilayer perceptronPulmonary tuberculosisMore Related Videos
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