混合Poisson分布及其应用：疾病的统计分布（五）

本文讨论了混合Ｐｏｉｓｓｏｎ分布的性质、应用条件、参数的估计及混合Ｐｏｉｓｓｏｎ分布阶数的确定，指出混合Ｐｏｉｓｓｏｎ分布可用于混合样本的判别归类，并用Ｂａｙｅｓ的思想导出其判别归类方法。模拟试验结果表明：当混合Ｐｏｉｓｓｏｎ分布中各部分的比例相差不大，而各部分的均值相差较大时，抽样效果和拟合效果越好，所得到的估计值越接近理论值；反之效果越差。     

Intelligent dialogue based on statistical models of clinical decision-making

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.     

Laboratory screening method for selection of healthy volunteers

The aim of laboratory screening in Phase I is to exclude subjects with subclinical illness, who might be at increased risk in the study, and who might also adversely influence interpretation of the results. A new method for laboratory screening, based on Bayesian probability theory, is proposed, which consists of: 1. Drawing up a list of diseases to be excluded. 2. Defining for each disease, the maximum acceptable risk that an included subject could be affected by it. 3. Identifying one test for each disease. 4. Using a contingency table to calculate the specificity of the test and integrating the estimated prevalence of the disease from epidemiological data. 5. Applying the percentage obtained by the calculation of specificity to the previously determined distribution of values in the volunteer population to identify the threshold value for inclusion. Use of this deductive method in screening volunteers for Phase I trials affords increased security of selection, while reducing the number of non-pertinent exclusions because of laboratory findings.     

Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure.

The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean +/- SD) 0.18 +/- 0.84, and 0.50 +/- 0.59 mm for STN exit point, which yields a 0.30 +/- 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders.     

Adaptive group sequential design for phase II clinical trials: A Bayesian decision theoretic approach

Bayesian decision theoretic approaches (BDTAs) have been widely studied in the literature as tools for designing and conducting phase II clinical trials. However, full Bayesian approaches that consider multiple endpoints are lacking. Since the monitoring of toxicity is a major goal of phase II trials, we propose an adaptive group sequential design using a BDTA, which characterizes efficacy and toxicity as correlated bivariate binary endpoints. We allow trade‐off between the two endpoints. Interim evaluations are conducted group sequentially, but the number of interim looks and the size of each group are chosen adaptively based on current observations. We utilize a loss function consisting of two components: the loss associated with accruing, treating, and monitoring patients, and the loss associated with making incorrect decisions. The performance of our Bayesian modeling, and the operating characteristics of decision rules under a wide range of loss function parameters are evaluated using seven scenarios in a simulation study. Our method is illustrated in the context of a single‐arm phase II trial of bevacizumab, gemcitabine, and oxaliplatin in patients with metastatic pancreatic adenocarcinoma. Copyright © 2009 John Wiley & Sons, Ltd.     

Parallel imaging reconstruction for arbitrary trajectories using k-space sparse matrices (kSPA).

Although the concept of receiving MR signal using multiple coils simultaneously has been known for over two decades, the technique has only recently become clinically available as a result of the development of several effective parallel imaging reconstruction algorithms. Despite the success of these algorithms, it remains a challenge in many applications to rapidly and reliably reconstruct an image from partially-acquired general non-Cartesian k-space data. Such applications include, for example, three-dimensional (3D) imaging, functional MRI (fMRI), perfusion-weighted imaging, and diffusion tensor imaging (DTI), in which a large number of images have to be reconstructed. In this work, a systematic k-space-based reconstruction algorithm based on k-space sparse matrices (kSPA) is introduced. This algorithm formulates the image reconstruction problem as a system of sparse linear equations in k-space. The inversion of this system of equations is achieved by computing a sparse approximate inverse matrix. The algorithm is demonstrated using both simulated and in vivo data, and the resulting image quality is comparable to that of the iterative sensitivity encoding (SENSE) algorithm. The kSPA algorithm is noniterative and the computed sparse approximate inverse can be applied repetitively to reconstruct all subsequent images. This algorithm, therefore, is particularly suitable for the aforementioned applications.     

基于贝叶斯多变量联合模型的体检人群脑卒中发病风险因素的纵向研究

背景 脑卒中是目前影响人类健康的主要公共卫生问题之一;健康体检纵向数据累积了大量的健康信息,由于缺失数据多、样本量小等诸多问题,导致其利用率低、重要信息未能得到充分挖掘,进而对常见慢性病的有效防控等工作带来一定困难。目的 基于贝叶斯多变量联合模型,探讨体检人群脑卒中发病风险因素,为慢性病风险因素分析提供新的方法。方法 本研究使用空军军医大学西京医院健康医学中心2008—2015年的体检资料。随访情况：以首次发生脑卒中为结局事件,发生结局事件立即停止随访;若未发生,到2015年体检信息收集完成后结束随访;体检间隔时间为1年。依据随访过程中是否发生脑卒中分为脑卒中组和非脑卒中组。纵向观察变量包括总胆固醇（TC）、三酰甘油（TG）、低密度脂蛋白胆固醇（LDL-C）、高密度脂蛋白胆固醇（HDL-C）、体质指数（BMI）和收缩压（SBP）。采用多因素Cox回归模型分析基线情况对脑卒中结局事件的影响;采用贝叶斯多变量联合模型,分析随访过程中TC、TG、LDL-C、HDL-C、BMI和SBP的纵向变化轨迹对脑卒中发病的影响。结果 本研究共纳入234例研究对象,1 581条纵向随访记录,平均随访时间为...     

Latent variable discovery in classification models

The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to a better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models.     

基于稀疏表示变量选择方法的影像遗传学数据分析

目的:采用影像遗传学研究方法探索精神分裂症的影像遗传学特征。方法:在传统稀疏回归模型的基础上，改进 了其在不同范数条件下进行变量选择的能力，形成一种基于稀疏表示变量选择算法，并将该算法应用于208 个受试者的 41 236个功能磁共振成像数据和722 177个单核苷酸多态性数据的综合分析。通过对两类数据施加不同的权重因子，并 使用不同的Lp （p=0、0.5、1）范数分别对模型进行求解，筛选出两类数据在不同条件下的显著特征。结果:基因DAOA和 HTR2A在3种范数下均被筛选出。此外，在影像学数据方面，发现中央前回、枕上回、顶下缘角回、角回、内侧和旁扣带脑 回、后扣带回脑区与精神分裂症相关，此发现与先前精神分裂症的临床医学研究结果一致。结论:基于稀疏表示变量选择 方法应用于影像遗传学数据分析是一个有效可行的途径，为今后精神分裂症的影像遗传学研究提供了一种新的研究 思路。     

Molecular evolution of SARS-CoV-2 from December 2019 to August 2022

Severe acute respiratorysyndrome coronavirus-2 (SARS-CoV-2) pandemic spread rapidly and this scenario is concerning worldwide, presenting more than 590 million coronavirus disease 2019 cases and 6.4 million deaths. The emergence of novel lineages carrying several mutations in the spike protein has raised additional public health concerns worldwide during the pandemic. The present study review and summarizes the temporal spreading and molecular evolution of SARS-CoV-2 clades and variants worldwide. The evaluation of these data is important for understanding the evolutionary histories of SARSCoV-2 lineages, allowing us to identify the origins of each lineage of this virus responsible for one of the biggest pandemics in history. A total of 2897 SARS-CoV-2 whole-genome sequences with available information from the country and sampling date (December 2019 to August 2022), were obtained and were evaluated by Bayesian approach. The results demonstrated that the SARS-CoV-2 the time to the most recent common ancestor (tMRCA) in Asia was 2019-12-26 (highest posterior density 95% [HPD95%]: 2019-12-18; 2019-12-29), in Oceania 2020-01-24 (HPD95%: 2020-01-15; 2020-01-30), in Africa 2020-02-27 (HPD95%: 2020-02-21; 2020-03-04), in Europe 2020-02-27 (HPD95%: 2020-02-20; 2020-03-06), in North America 2020-03-12 (HPD95%: 2020-03-05; 2020-03-18), and in South America 2020-03-15 (HPD95%: 2020-03-09; 2020-03-28). Between December 2019 and June 2020, 11 clades were detected (20I [Alpha] and 19A, 19B, 20B, 20C, 20A, 20D, 20E [EU1], 20F, 20H [Beta]). From July to December 2020, 4 clades were identified (20J [Gamma, V3], 21 C [Epsilon], 21D [Eta], and 21G [Lambda]). Between January and June 2021, 3 clades of the Delta variant were detected (21A, 21I, and 21J). Between July and December 2021, two variants were detected, Delta (21A, 21I, and 21J) and Omicron (21K, 21L, 22B, and 22C). Between January and June 2022, the Delta (21I and 21J) and Omicron (21K, 21L, and 22A) variants were detected. Finally, between July and August 2022, 3 clades of Omicron were detected (22B, 22C, and 22D). Clade 19A was first detected in the SARS-CoV-2 pandemic (Wuhan strain) with origin in 2019-12-16 (HPD95%: 2019-12-15; 2019-12-25); 20I (Alpha) in 2020-11-24 (HPD95%: 2020-11-15; 2021-12-02); 20H (Beta) in 2020-11-25 (HPD95%: 2020-11-13; 2020-11-29); 20J (Gamma) was 2020-12-21 (HPD95%: 2020-11-05; 2021-01-15); 21A (Delta) in 2020-09-20 (HPD95%: 2020-05-17; 2021-02-03); 21J (Delta) in 2021-02-26 (2020-11-02; 2021-04-24); 21M (Omicron) in 2021-01-25 (HPD95%: 2020-09-16; 2021-08-08); 21K (Omicron) in 2021-07-30 (HPD95%: 2021-05-30; 2021-10-19); 21L (Omicron) in 2021-10-03 (HPD95%: 2021-04-16; 2021-12-23); 22B (Omicron) in 2022-01-25 (HPD95%: 2022-01-10; 2022-02-05); 21L in 2021-12-20 (HPD95%: 2021-05-16; 2021-12-31). Currently, the Omicron variant predominates worldwide, with the 21L clade branching into 3 (22A, 22B, and 22C). Phylogeographic data showed that Alpha variant originated in the United Kingdom, Beta in South Africa, Gamma in Brazil, Delta in India, Omicron in South Africa, Mu in Colombia, Epsilon in the United States of America, and Lambda in Peru. The COVID-19 pandemic has had a significant impact on global health worldwide and the present study provides an overview of the molecular evolution of SARS-CoV-2 lineage clades (from the Wuhan strain to the currently circulating lineages of the Omicron).