UNASSIGNED: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.
UNASSIGNED: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.
UNASSIGNED: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
■轻度-中度AD患者前瞻性招募PSG,然后检测CSF和血液样本的生物标志物。PSG信号经过预处理以提取非线性,时域和频域统计量化特征。使用四个输入特征子集训练多个ML算法:临床变量(CLINVAR),常规PSG参数(SLEEPVAR),定量PSG信号特征(PSGVAR)和所有子集的组合(ALL)。采用交叉验证技术来评估模型性能并确保泛化性。开发回归模型以确定用于解释生物标志物中的方差的最有效的变量组合。
■关于49个科目,梯度提升回归因子在估计生物标志物水平方面取得了最好的结果,对每个生物标志物使用不同的损失函数:Aβ42的最小绝对偏差(LAD),p-tau的最小二乘(LS)和t-tau的Huber。ALL子集显示了所有三种生物标志物的最低训练误差,尽管具有不同的测试性能。具体来说,SLEEPVAR子集在预测Aβ42方面产生了最佳的测试性能,而ALL子集由于测试误差最低而最准确地预测了p-tau和t-tau.
■多模式ML可以通过利用非侵入性和经济上可行的变量来帮助预测早期AD中CSF生物标志物的结果。将计算模型集成到医疗实践中,为筛查有AD风险的患者提供了有希望的工具。潜在的指导临床决策。