Kruse Christian, Eiken Pia, Vestergaard Peter
Department of Endocrinology, Aalborg University Hospital, Moelleparkvej 4, 9000, Aalborg, Denmark.
Department of Clinical Medicine, Aalborg University, Sdr. Skovvej 15, 9000, Aalborg, Denmark.
Calcif Tissue Int. 2017 Apr;100(4):348-360. doi: 10.1007/s00223-017-0238-7. Epub 2017 Feb 14.
Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis ("bagFDA") performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Naïve Bayes adjustments. A "bagFDA" model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting ("xgbTree") performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an "xgbTree" model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.
应用机器学习原理预测髋部骨折，并评估双能X线吸收法（DXA）扫描的男性和女性中预测因素的重要性。将1996年至2006年丹麦两个地区的双能X线吸收法数据与丹麦国家患者数据相结合，纳入4722名女性和717名男性，随访时间为5年（原始队列中男性和女性共6606名）。通过k-5、5次重复交叉验证，基于75%的数据点构建了24个统计模型，然后在其余25%的数据点上进行验证，以计算曲线下面积（AUC）并校准概率估计。使用受限预测变量子集对最佳模型进行重新训练，以估计最佳子集。对于女性，自助聚合灵活判别分析（“bagFDA”）表现最佳，测试AUC为0.92[0.89；0.94]，经朴素贝叶斯调整后概率校准良好。一个限于11个预测因素（包括骨密度（BMD）、生化血糖测量、全科医生和牙医使用情况）的“bagFDA”模型，测试AUC为0.91[0.88；0.93]。对于男性，极端梯度提升（“xgbTree”）表现最佳，测试AUC为0.89[0.82；0.95]，但高概率下校准较差。使用“xgbTree”模型，一个包含10个预测因素的子集（BMD、生化胆固醇和肝功能测试、青霉素使用情况和骨关节炎诊断）的测试AUC为0.86[0.78；0.94]。机器学习可以使用集成模型在逻辑回归之外改善髋部骨折预测。汇集来自随访时间更长的国际队列的数据并执行类似的机器学习程序，有可能进一步提高判别力和校准。
