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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Medicine / Faculty of Medicine >
Peer-reviewed Journal Articles, etc >
Machine Learning Algorithms : Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
| Title: | Machine Learning Algorithms : Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture |
| Authors: | Shimizu, Hirokazu Browse this author | | Enda, Ken Browse this author | | Shimizu, Tomohiro Browse this author →KAKEN DB | | Ishida, Yusuke Browse this author →KAKEN DB | | Ishizu, Hotaka Browse this author | | Ise, Koki Browse this author | | Tanaka, Shinya Browse this author →KAKEN DB | | Iwasaki, Norimasa Browse this author →KAKEN DB |
| Keywords: | machine learning algorithms | | fragility fracture | | clinical refracture | | LightGBM | | rheumatoid arthritis | | chronic kidney disease |
| Issue Date: | 5-Apr-2022 |
| Publisher: | MDPI |
| Journal Title: | Journal of clinical medicine |
| Volume: | 11 |
| Issue: | 7 |
| Start Page: | 2021 |
| Publisher DOI: | 10.3390/jcm11072021 |
| Abstract: | Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture. Methods: Based on the fact that machine learning algorithms are often used for the analysis of a large-scale dataset, we developed automatic prediction models and clarified the relevant features for patients with clinical refracture. Formats of input data containing perioperative clinical information were table data. Clinical refracture was documented as the primary outcome if the diagnosis of fracture was made at postoperative outpatient care. A decision-tree-based model, LightGBM, had moderate accuracy for the prediction in the test and the independent dataset, whereas the other models had poor accuracy or worse. Results: From a clinical perspective, rheumatoid arthritis (RA) and chronic kidney disease (CKD) were noted as the relevant features for patients with clinical refracture, both of which were associated with secondary osteoporosis. Conclusion: The decision-tree-based algorithm showed the precise prediction of clinical refracture, in which RA and CKD were detected as the potential predictors. Understanding these predictors may improve the management of patients with fragility fractures. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85623 |
| Appears in Collections: | 医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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