Office for Cancer Diagnosis and Treatment Quality Control, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Comprehensive Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
J Healthc Eng. 2022 Aug 19;2022:6278854. doi: 10.1155/2022/6278854. eCollection 2022.
Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors.
In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software.
The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of "rbf" and "nu-regression" composition. Two key values (nu and gamma) and case number displayed high adjusted in curve regressions ( = 0.5649 × , gamma = 9.005 × 10 × case - 4.877 × 10 × case). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC).
We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.
免疫检查点抑制剂，如程序性死亡受体-1/配体-1（PD-1/L1），表现出自免疫样紊乱，高血糖是 3 级或更高级别的免疫相关不良事件之首。机器学习是一种基于过去数据进行未来数据预测的模型。从上市后监测来看，我们旨在构建一种机器学习算法，以便使用 PD-1/L1 抑制剂的患者高效、快速地预测高血糖不良反应。
在从美国食品和药物管理局不良事件报告系统（US FAERS）下载的原始数据中，使用支持向量机（SVM）的多元模式分类来构建一个分类器，以分离有不良高血糖反应的患者。在 R 语言软件中，使用正确的核心 SVM 功能构建模型设置，进行 10 折 3 次交叉验证优化参数值组合。
SVM 预测模型是从数字类型/数字优化方法、核以及“rbf”和“nu-regression”类型的组成建立的。两个关键值（nu 和 gamma）和病例数在曲线回归中显示出高调整值（=0.5649×，gamma=9.005×10×病例-4.877×10×病例）。该 SVM 模型具有可计算的参数，大大提高了评估指标（准确性、F1 得分和kappa）以及同等敏感性和曲线下面积（AUC）。
我们构建了一个基于精确核和可计算参数组合的有效机器学习模型；SVM 预测模型可以非侵入性和准确地预测接受 PD-1/L1 抑制剂治疗的患者的高血糖药物不良反应（ADR），这可以极大地帮助临床医生识别高危患者，并及时采取预防措施。此外，该模型建立过程为推广到其他 ADR 预测提供了一个分析思路，如识别高危患者对改善结局至关重要，该机器学习算法最终可以为临床决策提供价值。
