Arthroscopy. 2022 Mar;38(3):848-849. doi: 10.1016/j.arthro.2021.10.008.
Recent research using machine learning and data mining to determine predictors of prolonged opioid use after arthroscopic surgery showed that Artificial Neural Networks showed superior discrimination and calibration. Other machine learning algorithms, such as Naïve Bayes, XG Boost, Gradient Boosting Model, Random Forest, and Elastic Net, were also reliable despite slightly lower Brier scores and mean areas under the curve. Machine learning and data mining have limitations, however, and outputs are reliant on large sample sizes and the accuracy of big data. Poor-quality data and the lack of confounding variables are further limitations. There is no doubt that predictive modeling, artificial intelligence, machine learning, and data mining will become a major component of the physician's practice, and doctors of medicine and related researchers should become familiar with these techniques. Physicians require an understanding of data science for the following reasons: monitoring of large databases could allow early diagnosis of pathologic conditions in individual patients; multiparameter data can be used to assist in the development of care pathways; data visualization could help with interpretation of medical images; understanding artificial intelligence workflow and machine learning will help us with understanding early warning signs of disease; and data science will facilitate personalized medicine with which clinicians can predict treatment outcomes.
最近的研究使用机器学习和数据挖掘来确定关节镜手术后延长阿片类药物使用的预测因素，结果表明人工神经网络具有优越的判别力和校准能力。其他机器学习算法，如朴素贝叶斯、XG Boost、梯度提升模型、随机森林和弹性网络，尽管 Brier 分数和曲线下平均面积略低，但也同样可靠。然而，机器学习和数据挖掘有其局限性，输出结果依赖于大数据的大样本量和准确性。数据质量差和缺乏混杂变量是进一步的限制。毫无疑问，预测建模、人工智能、机器学习和数据挖掘将成为医生实践的主要组成部分，医学博士和相关研究人员应该熟悉这些技术。医生需要了解数据科学，原因如下：监测大型数据库可以允许对个体患者的病理状况进行早期诊断；多参数数据可用于辅助护理路径的制定；数据可视化有助于解释医学图像；了解人工智能工作流程和机器学习将有助于我们了解疾病的早期预警信号；数据科学将促进个性化医疗，临床医生可以预测治疗结果。
