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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.11760 |
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| _version_ | 1866912175401992192 |
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| author | Donié, Cedric Reumann, Marie K. Hartung, Tony Braun, Benedikt J. Histing, Tina Endo, Satoshi Hirche, Sandra |
| author_facet | Donié, Cedric Reumann, Marie K. Hartung, Tony Braun, Benedikt J. Histing, Tina Endo, Satoshi Hirche, Sandra |
| contents | Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union. The models provided prediction results with 70% sensitivity, and the specificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery Donié, Cedric Reumann, Marie K. Hartung, Tony Braun, Benedikt J. Histing, Tina Endo, Satoshi Hirche, Sandra Machine Learning J.3; I.5.4 Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union. The models provided prediction results with 70% sensitivity, and the specificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol. |
| title | Predictive Model Development to Identify Failed Healing in Patients after Non-Union Fracture Surgery |
| topic | Machine Learning J.3; I.5.4 |
| url | https://arxiv.org/abs/2404.11760 |