Conditional-quantile screening for ultrahigh-dimensional survival data via martingale difference correlation

Using the so-called martingale difference correlation(MDC), we propose a novel censoredconditional-quantile screening approach for ultrahigh-dimensional survival data with heterogeneity(which is often present in such data). By incorporating a weighting scheme, this method is a natural extension of MDCbased conditional quantile screening, as considered by Shao and Zhang(2014), to handle ultrahigh-dimensional survival data. The proposed screening procedure has a sure-screening property under certain technical conditions and an excellent capability of detecting the nonlinear relationship between independent and censored dependent variables. Both simulation results and an analysis of real data demonstrate the effectiveness of the new censored conditional quantile-screening procedure.

Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019

The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening potential patients and early detection of cases. This could also save the lives of elderly patients who have a higher mortality rate from this disease.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

Survival and comorbidities in lung cancer patients:Evidence from administrative claims data in Germany

Lung cancer is the most common cancer type worldwide and has the highest and second highest mortality rate for men and women respectively in Germany.Yet,the role of comorbid illnesses in lung cancer patient prognosis is still debated.We analyzed administrative claims data from one of the largest statutory health insurance(SHI)funds in Germany,covering close to 9 million people(11%of the national population);observation period was from 2005 to 2019.Lung cancer patients and their concomitant diseases were identified by ICD-10-GM codes.Comorbidities were classified according to the Charlson Comorbidity Index(CCI).Incidence,comorbidity prevalence and survival are estimated considering sex,age at diagnosis,and place of residence.Kaplan Meier curves with 95%confidence intervals were built in relation to common comorbidities.We identified 70,698 lung cancer incident cases in the sample.Incidence and survival figures are comparable to official statistics in Germany.Most prevalent comorbidities are chronic obstructive pulmonary disease(COPD)(36.7%),followed by peripheral vascular disease(PVD)(18.7%),diabetes without chronic complications(17.4%),congestive heart failure(CHF)(16.5%)and renal disease(14.7%).Relative to overall survival,lung cancer patients with CHF,cerebrovascular disease(CEVD)and renal disease are associated with largest drops in survival probabilities(9%or higher),while those with PVD and diabetes without chronic complications with moderate drops(7%or lower).The study showed a negative association between survival and most common comorbidities among lung cancer patients,based on a large sample for Germany.Further research needs to explore the individual effect of comorbidities disentangled from that of other patient characteristics such as cancer stage and histology.

Survivals after liver transplantation for hepatocellular carcinoma:Granular data for a better allocation process?

To the Editor:A large international study has been recently published focusing on the combination of morphological aspects and alpha-fetoprotein(AFP)as predictors of survival in patients with hepatocellular cancer(HCC)treated with liver transplantation(LT)[1].As a matter of fact,morphology and biology represent the two sides of the same

Artificial Neural Network Model for Predicting Lung Cancer Survival

The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.

Analysis of Influencing Factors on Survival Time of Patients with Heart Failure

To explore the influencing factors of survival time of patients with heart failure, a total of 1789 patients with heart failure were collected from Shanghai Shuguang Hospital. The Cox proportional hazards model and the mixed effects Cox model were used to analyze the factors on survival time of patients. The results of Cox proportional hazards model showed that age (RR = 1.32), hypertension (RR = 0.67), ARB (RR = 0.55), diuretic (RR = 1.48) and antiplatelet (RR = 0.53) have significant impacts on the survival time of patients. The results of mixed effects Cox model showed that age (RR = 1.16), hypertension (RR = 0.61), lung infection (RR = 1.43), ARB (RR = 0.64), β-blockers (RR = 0.77) and antiplatelet (RR = 0.69) have a significant impact on the survival time of patients. The results are consistent with the covariates age, hypertension, ARB and antiplatelet but inconsistent with the covariates lung infection and β-blockers.

Long-term survival of early-stage non-small cell lung cancer patients who underwent robotic procedure:a propensity score-matched study

Background:In the past decade,many researchers focused on to robot-assisted surgery.However,on long-term outcomes for patients with early-stage non-small cell lung cancer(NSCLC),whether the robotic procedure is superior to video-assisted thoracic surgery(VATS) and thoracotomy is unclear.Nonetheless,in the article titled "Long-term survival based on the surgical approach to lobectomy for clinical stage I non-small cell lung cancer:comparison of robotic,video assisted thoracic surgery,and thoracotomy lobectomy" by Yang et al.that was recently published in Annals of Surgery,the authors provided convincing evidence that the robotic procedure results in similar long-term survival as compared with VATS and thoracotomy.Minimally invasive procedures typically result in shorter lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Main body:Our propensity score-matched study generated high-quality data.Based on our findings,we see promise in expanding patient access to robotic lung resections.In this study,propensity score matching minimized the bias involved between groups.Nevertheless,due to its retrospective nature,bias may still exist.Currently,the concept of rapid rehabilitation is widely accepted,and it is very difficult to set up a randomized controlled trial to compare robotic,VATS,and thoracotomy procedures for the treatment of NSCLC.Therefore,to overcome this limitation and to minimize bias,the best approach is to use a registry and prospectively collected,propensity score-matched data.Conclusions:Robotic lung resections result in similar long-term survival as compared with VATS and thoracotomy.Robot-assisted and VATS procedures are associated with short lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Considering the alarming increase in the incidence of lung cancer in China,a nationwide database of prospectively collected data available for clinical research would be especially important.

Estimation of actual carbon dioxide removal in burned forests using satellite data:A case study in South Korea

With the increasing impact of climate change,carbon emissions and removals have become major issues.Forests are major carbon pools,and forest fires are an essential part of the carbon cycle.This study introduces a model for estimating the detailed actual CO_(2)removal in burned forests using burn severity and tree survivability.Actual CO_(2)removal was estimated from empirical yield tables without using the standard carbon removal provided by the national inventory.The primary CO_(2)calculation method followed the guidelines of the International Panel on Climate Change.The burn severity was mapped using Sentinel-2 multispectral instrument data,and the survivability of each forest type was estimated according to burn severity.The survivability was applied to the pre-fire CO_(2)removal of each forest to estimate post-fire CO_(2)removal.In our case study,the burned forest area was 1,034 ha,and the CO_(2)removal before the fire was 8,615.3t/year.After the fire,removal decreased by 81.2%to 1,618.4 t/yr.In particular,the decrease in coniferous forests was high,more than 86%.The lack of survivability data on burned trees was a major limitation of our study.Systematically accumulating field monitoring data of post-fire forests will be necessary for future research and could serve as a reference for devising immediate countermeasures against forest fires.

基于多模态数据融合与机器学习的高价值专利早期识别方法

[研究目的]如何构建有效的高价值专利识别方法,从海量的专利中识别出高价值专利,对于高价值专利的培育、运用、保护和管理具有重要意义。[研究方法]提出一种基于多模态数据融合与机器学习的高价值专利早期识别方法。首先,利用多模态融合方法对专利指标数据和文本数据两种模态数据进行融合;其次,利用基于机器学习的生存预测算法对历史已知的高价值专利进行分析,并获取高价值专利融合后的特征与其“高价值”之间的关联模式;最后,利用获取到的关联模式,对新公开专利的“价值”进行预判,从而实现对高价值专利的早期识别。[研究结论]以人工智能领域专利为例进行实证研究,验证了该方法的可行性和有效性,为高价值专利早期识别提供了新的研究方法。

基于随机生存森林的供水管道漏损风险评估

供水管网漏损正得到越来越多的关注,管道漏损不仅造成水资源的浪费,还可能引发城市安全事故,而管道漏损风险评估是供水企业进行漏损控制的重要步骤。在供水管道的生存分析中,未发生漏损的管道被认为是删失数据,而在构建漏损风险评估模型时未漏损的管道数据同样十分重要。研究利用生存分析能够处理删失数据的特点,采用随机生存森林算法构建H市供水管网漏损风险评估模型。结果表明,该模型有良好的预测精度,模型C指数超0.75。并且模型能准确识别影响漏损的关键因素,变量重要性结果说明管道固有属性比环境变量对管道漏损的影响程度更大。研究进一步分析了在不同温度和降雨下漏损风险动态变化,将预测漏损风险分为5个等级,发现寒冷干燥气候下管网中漏损风险等级Ⅱ和Ⅲ级管道数量最多,说明供水管网在冬季干燥天气下发生漏损概率最大。模型还预测管道在未来不同管龄下的生存概率,通过设定生存概率阈值判定何时发生漏损,结果表明短期漏损问题尚未十分严重,但10年后H市供水管网中漏损管道数量将明显增多。该研究为供水企业提供了长短期的漏损风险预测,并考虑环境因素对漏损的影响,有助于制定更为精确的管道维护和管道更新策略。

基于共享随机效应模型的纵向认知标志物对轻度认知障碍逆转的预测性能比较

目的将共享随机效应模型(shared random-effect model,SREM)应用于轻度认知障碍(mild cognitive impairment,MCI)向认知正常(normal cognition,NC)逆转的研究,比较不同纵向认知标志物对MCI逆转的预测性能,并评价影响因素的协变量效应。方法SREM模型包括两个子模型,其中纵向子模型采用线性混合效应模型对纵向认知标志物的变化轨迹建模,生存子模型采用比例风险模型对生存过程建模。基于对数似然函数值和信息准则进行模型拟合优度检验,采用ROC曲线下面积(area under the curve,AUC)评价不同纵向认知标志物(MMSE、CDRSB、FAQ、ADAS11、ADAS13和ADASQ4)对MCI逆转的预测性能;同时进行纵向子模型和生存子模型的影响因素分析。结果843名MCI患者中72名(8.54%)在随访结束后逆转为NC。以spline-PH-GH参数分布为基准风险函数的SREM模型对数似然函数值最大,AIC和BIC最小;以CDRSB为纵向认知标志物建立的SREM模型拟合最好,在不同时间的AUC值均表现良好,范围为0.797~0.852,且预测误差最小,范围为0.0427~0.0429;年龄、性别、受教育程度、婚姻状况和APOEε4基因均会影响MCI患者的认知功能和日常活动功能,六种纵向认知标志物均会影响MCI患者的逆转。结论CDR评分对MCI患者的认知功能和逆转预测性能最佳;认知功能和日常活动功能可作为MCI逆转的动态监测指标。

基于一种距离相关的超高维生存数据Model-Free特征筛选

随着大数据时代的来临,数据维度爆炸式增长,超高维数据的降维问题逐渐成为众多研究领域的热点话题。由于响应变量通常存在右删失,处理超高维完全数据的降维方法在右删失数据中将不再适用。本研究提出一种新的基于距离相关能有效处理超高维右删失数据的特征筛选方法。首先利用距离相关系数计算每个协变量对响应变量的边际效应,建立与该系数有关的筛选指标,然后再根据事先确立的筛选准则进行特征筛选。提出的特征筛选方法不依赖任何模型结构假定,因此可以有效避免模型指定错误带来的不良后果。此外,该方法采用的距离协方差估计量是总体距离协方差的一个无偏估计,统计准确性和计算精度高。模拟和实证研究表明,提出的方法能在保留所有重要变量的前提下快速剔除与响应变量相关程度较弱的协变量,从而达到降低参数维数的目的。

出场与匡正:大学生网络道德冷漠的数字化生存境遇

随着网络技术对生活世界的深度介入,数据日渐成为日常生活的社会基础,在万物皆数、数智融合、万物智联的大数据语境中,数字化生存时代的“算法合谋”“感觉新变”“时空转向”不仅引致大学生网络道德认知力弱化、网络道德情感力淡化、网络道德行动力软化,而且加重了网络社会的去道德化与网络道德生活的去规范化。数字化生存时代的实践场域中克服由网络道德认知上的错乱、网络道德情感上的麻木以及网络道德行动上的逃避引致的网络道德冷漠,亟须实现网络道德认知的数字化转向、网络道德情感的美学化转向、网络道德行动的智慧化转向,从而构建良好网络生态、营造清朗网络空间。

Construction and application of Markov model for followup data

Objective: To construct Markov model for clinical follow up data and predict the number of survivals and deaths. Methods: The state of patients was classified into survival. death and censorship. The weighted least squares method was used for estimating parameters. Results: Markov model for survival analysis of follow--up data was presented. By using an example, the transition probability matrices were obtained and the number of survivals and deaths at each observation point was predicted respectively. Conclusion: Markov model constructed in the present study to analyze clinical follow up data could be used as effective supplemention for life table analysis.

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve

BACKGROUND Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer.Two clinical trials have evaluated lutetium thus far(therap and vision with 99 and 385 patients,respectively),but their results are discordant.AIM To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer;and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data.METHODS We employed a new artificial intelligence method(shiny method)to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another.The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves.The progression-free survival graphs of the two lutetium cohorts were analyzed and compared.RESULTS The hazard ratio estimated was in favor of the vision trial;the difference was statistically significant(P<0.001).These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting.CONCLUSION Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics.

电力通信链路模拟数据智能融合方法

为了提高电力通信系统数据融合时的节点存活率,提高网络连通性,提出了基于异构数据源的电力通信链路模拟数据智能融合方法。利用最小二乘残差估计法辨别噪声数据,识别异构数据中的不良数据。通过k-means聚类获取目标平均值,实现数据离散化并消除噪声。使用频繁项集的关联规则确定置信度阈值并挖掘不低于该阈值的数据。采用深度受限玻尔兹曼机算法将不同类型模拟数据映射到同一矢量空间内,实现智能融合。仿真实验结果表明,该数据融合方法的平均系统能量消耗为66.35 J,网络连通度范围为0.85~1,达到了提高节点存活率以及提升网络连通性的目的。

融合多组学数据的乳腺癌生存期预测模型研究

精准预后对破解乳腺癌变化的复杂机制和制定个性化医疗方案有重要意义,基于多组学数据对乳腺癌患者进行早期诊断和精准预后已成为近几年乳腺癌研究的一个重要研究方向.该文提出正类预测概率加权融合模型,将其用于融合乳腺癌患者的基因表达、拷贝数变异和临床信息数据,并预测患者生存期.该文在对3个数据集分别进行数据清洗、缺失值插补和降维处理后,从Logistic等5种机器学习分类算法中筛选出在单一数据集上效果最优的分类预测模型,然后以最优模型的正类预测概率为权数构建正类预测概率加权融合模型,将加权平均结果作为最终预测结果,其中权重的确定依赖于各模型的准确度以及它们之间的互信息.正类预测概率加权融合模型不仅同时利用了3个数据集的信息并规避了数据集异质性的问题,还在确定权重的过程中综合考虑了每个模型的准确度及它们之间的互信息,有效提升了乳腺癌患者生存期预测效果.

基于多元大数据融合的智能电能表可靠性评估模型

智能电能表是电能计量体系的基础单元,广泛部署于用户侧。针对其量大而难以维护的问题,建立了基于多源大数据融合分析的智能电能表可靠性评估模型。为了充分发掘智能电能表设计、检修和运行数据中的有用信息,对多源大数据进行融合整理,得到了影响智能电能表寿命的协变量数据和智能电能表生存标签。基于生存分析理论建立智能电能表生命周期CoxPH模型,并采用深度神经网络表征强非线性关联参数,形成智能电能表的可靠性评估模型。基于某城市实际智能电能表运维数据,对所建模型的有效性进行了验证。测试结果表明,所建模型可以基于智能电能表的实时运行状态实现可靠性评估,为智能电能表运维工作提供辅助决策。

基于多组学数据和稀疏变分自编码器的生存分析算法

针对生存分析中多组学数据带来的维数灾难和过拟合问题,提出了一种基于多组学数据和稀疏变分自编码器的生存分析算法VAESCox。该算法将变分自编码器的基本结构与稀疏编码和生存分析相结合,在无监督阶段训练变分自编码器学习低维表示,在监督阶段将训练的权重迁移到生存分析模型,并对传递权重进行微调和稀疏编码。实验结果表明,在八种不同癌症类型的数据集上,VAESCox模型在消融和对比实验中均取得了较高的C指数值。与其他四种基准生存分析方法相比,所提算法不仅缓解了多组学数据融合的过拟合问题,也显著提高了生存预测性能,表明不同组学数据的融合有助于预后生存结果的精准预测。