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Predicting amputation using machine learning: A systematic review
Patrick Fangping Yao1, Yi David Diao1, Eric P McMullen1
1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Machine learning (ML) models show promise in predicting amputation risk across various conditions, outperforming traditional methods. Further research is needed to improve model reliability and clinical application for this critical surgical outcome.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Surgical Outcomes Prediction
Background:
- Amputation is a last-resort treatment for numerous medical conditions.
- Delayed amputation can increase patient morbidity and mortality risks.
Conclusions:
- Developed ML models show potential to surpass traditional techniques and clinical judgment in predicting amputation.
- Many reviewed studies exhibited a high risk of bias, primarily due to small sample sizes.
- Further research is essential to address current study limitations and facilitate clinical implementation of ML for amputation prediction.
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