ArticleHealthcare informatics research2018
Comparison of Models for the Prediction of Medical Costs of Spinal Fusion in Taiwan Diagnosis-Related Groups by Machine Learning Algorithms.
Article in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
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- Artificial Learning and Machine Learning Applications in Spine Surgery: A Systematic Review.Global spine journal · 2022Article
- Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery.Global spine journal · 2022Article
- Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.Journal of personalized medicine · 2022Review
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- Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning.Journal of healthcare engineering · 2022Article
- The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients.Healthcare (Basel, Switzerland) · 2021Article
- Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.Journal of personalized medicine · 2020Article
- Impact of industry 4.0 to create advancements in orthopaedics.Journal of clinical orthopaedics and trauma · 2020Article
- Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model.World journal of clinical cases · 2020Article
- Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions.Current reviews in musculoskeletal medicine · 2020Review
- Positive predictive value of ICD-10 codes for acute myocardial infarction in Japan: a validation study at a single center.BMC health services research · 2018Article
- Fast Convolutional Method for Automatic Sleep Stage Classification.Healthcare informatics research · 2018Article
- Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA.Healthcare informatics research · 2018Article
- Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare.Health services research and managerial epidemiologyArticle
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4 authors.
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Abstract
objectivesThe aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion.
methodsA data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1.
resultsFive hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904.
conclusionsOur study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.
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