Pruneski James A, Pareek Ayoosh, Kunze Kyle N, Martin R Kyle, Karlsson Jón, Oeding Jacob F, Kiapour Ata M, Nwachukwu Benedict U, Williams Riley J
Department of Orthopedic Surgery, Boston Children's Hospital, Boston, MA, USA.
Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
Knee Surg Sports Traumatol Arthrosc. 2023 Apr;31(4):1196-1202. doi: 10.1007/s00167-022-07181-2. Epub 2022 Oct 12.
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
监督学习是医学研究中使用的最常见的机器学习形式。它用于预测感兴趣的结果或根据已知的真实情况对阳性和/或阴性病例进行分类。监督学习描述了一系列技术，从传统的回归建模到更复杂的树增强，随着对“大数据”的关注发展，这些技术越来越普遍。虽然这些工具越来越受欢迎且功能强大，但描述这些不同建模技术的优势和局限性的文献却很少。通常，医疗保健专业人员在使用机器学习模型方面没有接受过正规培训。随着机器学习在整个医学领域的应用增加，医生和其他医疗保健专业人员更好地理解这些技术应用背后的过程变得很重要。本研究的目的是通过骨科文献中的近期案例示例，概述常用的监督学习技术。另一个目标是解决对这些方法理解上的差异，以改善研究团队内部和之间的沟通。
