Liao PH, Tsuei YC, Chu W. Application of Machine Learning in Developing Decision-Making Support Models for Decompressed Vertebroplasty.
Healthcare (Basel) 2022;
10:214. [PMID:
35206831 PMCID:
PMC8872006 DOI:
10.3390/healthcare10020214]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND
The common treatment methods for vertebral compression fractures with osteoporosis are vertebroplasty and kyphoplasty, and the result of the operation may be related to the value of various measurement data during the operation.
MATERIAL AND METHOD
This study mainly uses machine learning algorithms, including Bayesian networks, neural networks, and discriminant analysis, to predict the effects of different decompression vertebroplasty methods on preoperative symptoms and changes in vital signs and oxygen saturation in intraoperative measurement data.
RESULT
The neural network shows better analysis results, and the area under the curve is >0.7. In general, important determinants of surgery include numbness and immobility of the lower limbs before surgery.
CONCLUSION
In the future, this association model can be used to assist in decision making regarding surgical methods. The results show that different surgical methods are related to abnormal vital signs and may affect the length of hospital stay.
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