This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） w...This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.展开更多
为解决传统初始地应力场反演方法存在边界条件筛选能力弱、易受数据过拟合干扰以及难以解析多重边界相互作用的问题,提出一种基于LASSO-OLS(least absolute shrinkage and selection operator-ordinary least squares)的两阶段初始地应...为解决传统初始地应力场反演方法存在边界条件筛选能力弱、易受数据过拟合干扰以及难以解析多重边界相互作用的问题,提出一种基于LASSO-OLS(least absolute shrinkage and selection operator-ordinary least squares)的两阶段初始地应力场反演方法。该方法首先通过对候选边界条件应力矩阵和实测应力矩阵进行Frobenius范数标准化处理,消除不同边界条件数据量级差异的影响;然后,利用LASSO回归的L1正则化约束,从候选边界条件的回归系数路径图中筛选关键影响因素,剔除冗余与弱相关项;最后,针对筛选出的核心变量,采用普通最小二乘回归进行无偏估计,构建兼具稀疏性与准确性的地应力场反演模型。研究结果表明:1)在工程应用实例中,借助LASSO回归从11个候选边界条件中筛选出5个关键因素,显著降低模型复杂度;2)模型正则化参数在标准误差内取值,拟合结果能够保持较高的复相关系数(R=0.995 2),表明筛选后的边界条件有效捕捉了初始地应力场特征;3)初始地应力场反演模型通过LASSO回归筛选,在解析多重边界相互作用时表现出较高的稳定性和物理合理性;4)与传统方法相比,该方法能有效避免初始地应力场反演出现过拟合问题,提高反演结果的鲁棒性。展开更多
目的:大学生非自杀性自伤(non-suicidal self-injury,NSSI)行为已成为重要的公共卫生问题,需建立有效的早期识别工具。本研究旨在基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析方法构建...目的:大学生非自杀性自伤(non-suicidal self-injury,NSSI)行为已成为重要的公共卫生问题,需建立有效的早期识别工具。本研究旨在基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析方法构建预测大学生NSSI行为的预测模型。方法:2022年4至6月期间,通过线上平台对湖南、江西、湖北、山东、广东和吉林6个省份在校大学生进行问卷调查。收集大学生的一般社会学人口资料,使用青少年非自杀性自伤行为评定问卷、患者健康问卷-9、愤怒反刍思维量表、多重形式暴力量表、儿童期虐待问卷简版及简式版社区心理体验评估问卷进行调查。通过LASSO回归分析筛选出大学生NSSI行为的预测因素,构建大学生NSSI行为的预测模型并绘制列线图。采用受试者操作特征(receiver operating characteristic,ROC)曲线和校准曲线对预测模型的区分度和校准度进行评估。结果:本研究共4 121名大学生参与,其中650名大学生存在NSSI行为,检出率为15.8%。LASSO回归分析结果显示:小学受欺凌经历、饮酒史、抑郁情绪、愤怒反刍思维和精神病样体验是大学生NSSI行为的预测因素。预测模型显示:大学生NSSI行为的发生风险=小学受欺凌经历×0.41+饮酒史×0.76+抑郁情绪×0.08+愤怒反刍思维×0.04+精神病样体验×0.05。ROC曲线结果表明:预测模型在训练集中的曲线下面积(area under the curve,AUC)为0.782,在测试集中AUC为0.769。校准曲线显示模型的预测值与实际值基本一致。结论:本研究构建的预测模型具有较好的预测能力,并通过列线图实现模型结果可视化呈现。该预测模型能够根据大学生NSSI行为的危险因素评估其风险,帮助临床医师或教育者及时发现高危人群并进行早期干预。展开更多
BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intr...BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.展开更多
基金Project(21465016)supported by the National Natural Foundation of China
文摘This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.
文摘为解决传统初始地应力场反演方法存在边界条件筛选能力弱、易受数据过拟合干扰以及难以解析多重边界相互作用的问题,提出一种基于LASSO-OLS(least absolute shrinkage and selection operator-ordinary least squares)的两阶段初始地应力场反演方法。该方法首先通过对候选边界条件应力矩阵和实测应力矩阵进行Frobenius范数标准化处理,消除不同边界条件数据量级差异的影响;然后,利用LASSO回归的L1正则化约束,从候选边界条件的回归系数路径图中筛选关键影响因素,剔除冗余与弱相关项;最后,针对筛选出的核心变量,采用普通最小二乘回归进行无偏估计,构建兼具稀疏性与准确性的地应力场反演模型。研究结果表明:1)在工程应用实例中,借助LASSO回归从11个候选边界条件中筛选出5个关键因素,显著降低模型复杂度;2)模型正则化参数在标准误差内取值,拟合结果能够保持较高的复相关系数(R=0.995 2),表明筛选后的边界条件有效捕捉了初始地应力场特征;3)初始地应力场反演模型通过LASSO回归筛选,在解析多重边界相互作用时表现出较高的稳定性和物理合理性;4)与传统方法相比,该方法能有效避免初始地应力场反演出现过拟合问题,提高反演结果的鲁棒性。
文摘目的:大学生非自杀性自伤(non-suicidal self-injury,NSSI)行为已成为重要的公共卫生问题,需建立有效的早期识别工具。本研究旨在基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析方法构建预测大学生NSSI行为的预测模型。方法:2022年4至6月期间,通过线上平台对湖南、江西、湖北、山东、广东和吉林6个省份在校大学生进行问卷调查。收集大学生的一般社会学人口资料,使用青少年非自杀性自伤行为评定问卷、患者健康问卷-9、愤怒反刍思维量表、多重形式暴力量表、儿童期虐待问卷简版及简式版社区心理体验评估问卷进行调查。通过LASSO回归分析筛选出大学生NSSI行为的预测因素,构建大学生NSSI行为的预测模型并绘制列线图。采用受试者操作特征(receiver operating characteristic,ROC)曲线和校准曲线对预测模型的区分度和校准度进行评估。结果:本研究共4 121名大学生参与,其中650名大学生存在NSSI行为,检出率为15.8%。LASSO回归分析结果显示:小学受欺凌经历、饮酒史、抑郁情绪、愤怒反刍思维和精神病样体验是大学生NSSI行为的预测因素。预测模型显示:大学生NSSI行为的发生风险=小学受欺凌经历×0.41+饮酒史×0.76+抑郁情绪×0.08+愤怒反刍思维×0.04+精神病样体验×0.05。ROC曲线结果表明:预测模型在训练集中的曲线下面积(area under the curve,AUC)为0.782,在测试集中AUC为0.769。校准曲线显示模型的预测值与实际值基本一致。结论:本研究构建的预测模型具有较好的预测能力,并通过列线图实现模型结果可视化呈现。该预测模型能够根据大学生NSSI行为的危险因素评估其风险,帮助临床医师或教育者及时发现高危人群并进行早期干预。
文摘针对葡萄籽油掺假的问题,研究了葵花籽油掺伪葡萄籽油掺假的定量分析的技术方法。采用便携式近红外拉曼光谱技术结合最小绝对收缩选择算子和最小二乘支持向量机(Least Absolute Shrinkage and Selection Operator-Least Squares Support Vector Machine,LASSO-LSSVM)算法开展了不同掺伪体积分数的模拟实验。实验中共制备了11种不同掺伪体积分数的混合油样,通过便携式785拉曼光谱仪分别采集所有油样的拉曼光谱,采用小波算法对原始光谱数据进行基线校正、降噪和归一化处理,引入机器学习算法分别提取拉曼光谱的特征向量并建立量化分析模型,LASSO算法实现光谱数据降维和特征提取,LSSVM算法则构建掺伪量化的分析模型,模型测试集的决定系数R^(2)为0.9846,RMSE为0.0249。基于LASSO-LSSVM算法实现了赋能拉曼光谱技术鉴别葡萄籽油的掺假量,该技术方法对促进国内葡萄籽油消费市场具有重要的应用价值和商业潜力。
基金the Chinese Clinical Trial Registry(No.ChiCTR2000040109)approved by the Hospital Ethics Committee(No.20210130017).
文摘BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.
