Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice
Background: Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) t...
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025000169 |
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| author | Guan-Heng Liu Chin-Ling Li Chih-Yuan Yang Shih-Feng Liu |
| author_facet | Guan-Heng Liu Chin-Ling Li Chih-Yuan Yang Shih-Feng Liu |
| author_sort | Guan-Heng Liu |
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| description | Background: Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) to forecast healthcare expenses in COPD patients. Methods: A retrospective analysis of 396 COPD patients was conducted, utilizing clinical, demographic, and comorbidity data. Missing data were addressed through advanced imputation techniques to minimize bias. The final predictors included interactions such as Age × BMI, alongside Tumor Presence, Number of Comorbidities, Acute Exacerbation frequency, and the DOSE Index. A Gradient Boosting model was constructed, optimized with Recursive Feature Elimination (RFE), and evaluated using 5-fold cross-validation on an 80/20 train-test split. Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Results: On the training set, the model achieved an MSE of 0.049, MAE of 0.159, MAPE of 3.41 %, and R² of 0.703. On the test set, performance metrics included an MSE of 0.122, MAE of 0.258, MAPE of 5.49 %, and R² of 0.365. Tumor Presence, Age, and BMI were identified as key predictors of cost variability. Conclusions: The MCPI demonstrates strong potential for predicting healthcare costs in COPD patients and enables targeted interventions for high-risk individuals. Future research should focus on validation with multicenter datasets and the inclusion of additional socioeconomic variables to enhance model generalizability and precision. |
| format | Article |
| id | doaj-art-44e9d245656d4333b5e114cb3158f752 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-44e9d245656d4333b5e114cb3158f7522025-02-02T05:27:03ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127541547Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practiceGuan-Heng Liu0Chin-Ling Li1Chih-Yuan Yang2Shih-Feng Liu3Department of Artificial Intelligence, Chang Gung University, Taoyuan 333, TaiwanDepartment of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, TaiwanDepartment of Artificial Intelligence, Chang Gung University, Taoyuan 333, Taiwan; Corresponding author.Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; Medical Department, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; Corresponding author at: Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.Background: Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) to forecast healthcare expenses in COPD patients. Methods: A retrospective analysis of 396 COPD patients was conducted, utilizing clinical, demographic, and comorbidity data. Missing data were addressed through advanced imputation techniques to minimize bias. The final predictors included interactions such as Age × BMI, alongside Tumor Presence, Number of Comorbidities, Acute Exacerbation frequency, and the DOSE Index. A Gradient Boosting model was constructed, optimized with Recursive Feature Elimination (RFE), and evaluated using 5-fold cross-validation on an 80/20 train-test split. Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Results: On the training set, the model achieved an MSE of 0.049, MAE of 0.159, MAPE of 3.41 %, and R² of 0.703. On the test set, performance metrics included an MSE of 0.122, MAE of 0.258, MAPE of 5.49 %, and R² of 0.365. Tumor Presence, Age, and BMI were identified as key predictors of cost variability. Conclusions: The MCPI demonstrates strong potential for predicting healthcare costs in COPD patients and enables targeted interventions for high-risk individuals. Future research should focus on validation with multicenter datasets and the inclusion of additional socioeconomic variables to enhance model generalizability and precision.http://www.sciencedirect.com/science/article/pii/S2001037025000169COPDMCPIGradient boosting modelRecursive Feature Elimination5-fold cross-validation |
| spellingShingle | Guan-Heng Liu Chin-Ling Li Chih-Yuan Yang Shih-Feng Liu Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice Computational and Structural Biotechnology Journal COPD MCPI Gradient boosting model Recursive Feature Elimination 5-fold cross-validation |
| title | Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice |
| title_full | Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice |
| title_fullStr | Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice |
| title_full_unstemmed | Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice |
| title_short | Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice |
| title_sort | development and validation of a novel ai derived index for predicting copd medical costs in clinical practice |
| topic | COPD MCPI Gradient boosting model Recursive Feature Elimination 5-fold cross-validation |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025000169 |
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