■临床试验,包括食物特异性皮肤和血清IgE水平测试,提供有限的准确性来预测食物过敏。通常需要确认性口服食物挑战(OFC),但是相关的风险,成本,后勤困难是正确诊断的障碍。
■我们试图利用先进的机器学习方法来整合与花生过敏相关的临床变量,以创建OFC的预测模型,从而提高预测性能,而不是纯粹的统计方法。
■机器学习应用于对463个花生OFC和相关临床变量的花生过敏早期学习(LEAP)研究。使用患者间交叉验证来创建在保持测试集上评估的集合模型。通过使用2个额外的花生过敏OFC队列来进一步评估这些模型:IMPACT研究队列和密歇根当地大学队列。
■在LEAP数据集中,集合模型的最大平均曲线下面积为0.997,敏感性和特异性分别为0.994和1.00.在组合的验证数据集中,顶部集合模型的最大曲线下面积为0.871,敏感性和特异性分别为0.763和0.980。
■用于预测花生OFC结果的机器学习模型有可能准确预测OFC结果,可能最大限度地减少对OFC的需求,同时增加对食物过敏诊断的信心。
UNASSIGNED: Clinical testing, including food-specific skin and serum IgE level tests, provides limited accuracy to predict food allergy. Confirmatory oral food challenges (OFCs) are often required, but the associated risks, cost, and logistic difficulties comprise a barrier to proper diagnosis.
UNASSIGNED: We sought to utilize advanced machine learning methodologies to integrate clinical variables associated with peanut allergy to create a predictive model for OFCs to improve predictive performance over that of purely statistical methods.
UNASSIGNED: Machine learning was applied to the Learning Early about Peanut Allergy (LEAP) study of 463 peanut OFCs and associated clinical variables. Patient-wise cross-validation was used to create ensemble models that were evaluated on holdout test sets. These models were further evaluated by using 2 additional peanut allergy OFC cohorts: the IMPACT study cohort and a local University of Michigan cohort.
UNASSIGNED: In the LEAP data set, the ensemble models achieved a maximum mean area under the curve of 0.997, with a sensitivity and specificity of 0.994 and 1.00, respectively. In the combined validation data sets, the top ensemble model achieved a maximum area under the curve of 0.871, with a sensitivity and specificity of 0.763 and 0.980, respectively.
UNASSIGNED: Machine learning models for predicting peanut OFC results have the potential to accurately predict OFC outcomes, potentially minimizing the need for OFCs while increasing confidence in food allergy diagnoses.