韩国患者腰椎管狭窄症手术短期预后预测的机器学习模型开发
Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients.
作者信息
Kim Kyeong-Rae, Kim Hyeun Sung, Park Jae-Eun, Kang Seung-Yeon, Lim So-Young, Jang Il-Tae
机构信息
Nanoori Medical Research Institute, Nanoori Hospital Gangnam, Seoul 06048, Korea.
Department of Neurosurgery, Nanoori Hospital Gangnam, Seoul 06048, Korea.
出版信息
Brain Sci. 2020 Oct 22;10(11):764. doi: 10.3390/brainsci10110764.
BACKGROUND
In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery.
METHODS
Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time.
RESULTS
Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model.
CONCLUSION
This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.
背景
在本研究中，基于机器学习技术，我们旨在开发一个对接受椎管狭窄手术的韩国患者短期预后的预测模型。
方法
利用2019年2月至11月在N医院收治的112例椎管狭窄患者的数据，对疼痛指数、再次手术和手术时间进行预测分析。
结果
结果显示，疼痛指数、再次手术和手术时间的预测曲线下面积分别为0.803、0.887和0.896，从而表明该模型的准确性。
结论
本研究证实患者的个体特征和手术中的治疗特征能够预测患者预后，并验证了该方法的准确性。应进行进一步研究，通过纳入更大、更准确的数据集来扩大本研究的范围。
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