摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-髋关节骨折的新研究是一篇报道的主题。根据来自瑞士苏黎世的Ne wsRx记者的新闻报道，研究表明：“骨折预测在治疗骨质疏松症患者中是必不可少的，也是许多骨折预防指南的组成部分。我们旨在通过在两个队列中训练和验证短期和长期骨折风险预测模型，确定当代人群中最相关的临床骨折RI SK因素。”我们的新闻记者引用了瑞士联邦苏黎世理工学院(ETH)的研究，“我们使用传统和机器学习的生存模型，根据瑞士全国骨质疏松症登记处参与者的临床风险因素、T评分和治疗史预测脊椎、髋关节和任何骨折的风险（N=5944名绝经后妇女。”2015年1月至2022年10月，中位随访4.1年；随访期间共1190例FR骨折）。独立验证队列包括来自英国生物银行的5474例绝经后妇女，随访期间发生290例骨折。计算Uno的C指数和受试者操作弹道曲线下的时间依赖面积，以评估不同机器学习模型（随机生存森林和极端梯度提升）的性能。在独立验证集中，第2年椎体骨折的C指数为0.74[0.58,0.86]，髋部骨折的C指数为0.83[0.7,0.94]，任何骨折的C指数为0.63[0.58,0.69]，并且这些值在长达7年的评估中进一步增加。相比之下，FRAX?Switzerland计算的10年骨折概率为0.60[0.55,0.64][0.490.74]对于髋部骨折。Shapley相加解释(SHAP)值确定的最重要变量是年龄、t评分和既往骨折，而跌倒次数是髋部骨折的重要预测因素。传统和机器学习模型的表现显示出相似的c指数。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Hip Fracture is the su bject of a report. According to news originating from Zurich, Switzerland, by Ne wsRx correspondents, research stated, "Fracture prediction is essential in manag ing patients with osteoporosis, and is an integral component of many fracture pr evention guidelines. We aimed to identify the most relevant clinical fracture ri sk factors in contemporary populations by training and validating short- and lon g-term fracture risk prediction models in two cohorts." Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology Zurich (ETH), "We used traditional and machine learning s urvival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1 years between January 2015 and October 2022; a total of 1190 fr actures during follow-up). The independent validation cohort comprised 5474 post menopausal women from the UK Biobank with 290 incident fractures during follow-u p. Uno's C-index and the time-dependent area under the receiver operating charac teristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86 ] for vertebral fractures, 0.83 [0.7, 0.94 ] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estim ations of up to 7 years. In comparison, the 10- year fracture probability calcula ted with FRAX? Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both trad itional and machine learning models showed similar C-indices."
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