Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data

According to the American Cancer Society report (1999), cancer surpasses heart disease as the leading cause of death in the United States of America (USA) for people of age less than 85. Thus, medical research in cancer is an important public health interest. Understanding how medical improvements are affecting cancer incidence, mortality and survival is critical for effective cancer control. In this paper, we study the cancer survival trend on the population level cancer data. In particular, we develop a parametric Bayesian joinpoint regression model based on a Poisson distribution for the relative survival. To avoid identifying the cause of death, we only conduct analysis based on the relative survival. The method is further extended to the semiparametric Bayesian joinpoint regression models wherein the parametric distributional assumptions of the joinpoint regression models are relaxed by modeling the distribution of regression slopes using Dirichlet process mixtures. We also consider the effect of adding covariates of interest in the joinpoint model. Three model selection criteria, namely, the conditional predictive ordinate (CPO), the expected predictive deviance (EPD), and the deviance information criteria (DIC), are used to select the number of joinpoints. We analyze the grouped survival data for distant testicular cancer from the Surveillance, Epidemiology, and End Results (SEER) Program using these Bayesian models.     

An improved random forest-based rule extraction method for breast cancer diagnosis

Breast cancer has been becoming the main cause of death in women all around the world. An accurate and interpretable method is necessary for diagnosing patients with breast cancer for well-performed treatment. Nowadays, a great many of ensemble methods have been widely applied to breast cancer diagnosis, capable of achieving high accuracy, such as Random Forest. However, they are black-box methods which are unable to explain the reasons behind the diagnosis. To surmount this limitation, a rule extraction method named improved Random Forest (RF)-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis. Firstly, numbers of decision tree models are constructed using Random Forest to generate abundant decision rules available. And then a rule extraction approach is devised to detach decision rules from the trained trees. Finally, an improved multi-objective evolutionary algorithm (MOEA) is employed to seek for an optimal rule predictor where the constituent rule set is the best trade-off between accuracy and interpretability. The developed method is evaluated on three breast cancer data sets, i.e., the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, Wisconsin Original Breast Cancer (WOBC) dataset, and Surveillance, Epidemiology and End Results (SEER) breast cancer dataset. The experimental results demonstrate that the developed method can primely explain the black-box methods and outperform several popular single algorithms, ensemble learning methods, and rule extraction methods from the view of accuracy and interpretability. What is more, the proposed method can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.     

SURVSOFT—Software for nonparametric survival analysis

Long-term observed and relative survival are important outcome measures of cancer patient care reported routinely by many cancer registries, but no commercial statistical software exists for estimating relative survival or performing period survival analysis. The programs publicly available focus only on certain methods, require specific input data formats and often are macros or functions which require underlying software packages. Here we introduce SURVSOFT, a comprehensive, user-friendly Windows program with graphical user interface. It can handle different input data formats and incorporates a variety of nonparametric statistical methods for survival data analysis. SURVSOFT produces high-resolution graphs, which can be printed, saved or exported to be used with standard graphics editors. The use of SURVSOFT is illustrated by the analysis of survival data from the Bavarian Cancer Registry.     

Mixture cure models for multivariate survival data

Mixture cure models (MCMs) have been widely used to analyze survival data with a cure fraction. The MCMs postulate that a fraction of the patients are cured from the disease and that the failure time for the uncured patients follows a proper survival distribution, referred to as latency distribution. The MCMs have been extended to bivariate survival data by modeling the marginal distributions. In this paper, the marginal MCM is extended to multivariate survival data. The new model is applicable to the survival data with varied cluster size and interval censoring. The proposed model allows covariates to be incorporated into both the cure fraction and the latency distribution for the uncured patients. The primary interest is to estimate the marginal parameters in the mean structure, where the correlation structure is treated as nuisance parameters. The marginal parameters are estimated consistently by treating the observations within the cluster as independent. The variances of the parameters are estimated by the one-step jackknife method. The proposed method does not depend on the specification of correlation structure. Simulation studies show that the new method works well when the marginal model is correct. The performance of the MCM is also examined when the clustered survival times share common random effect. The MCM is applied to the data from a smoking cessation study.     

CancerSpace: A Simulation-Based Game for Improving Cancer-Screening Rates

Serious games are seeing use in a variety of fields, from the military to corporate management, and are finally being employed in healthcare. One of the biggest, most challenging areas is modeling simulations for medical training, particularly for managing chronic illness and providing system-level population-based care. CancerSpace (Cancer: Simulating Practice and Collaborative Education) is an interactive, Web-based learning application in a game format. CancerSpace, developed by the National Cancer Institute and Oak Ridge Institute for Science and Education, aims to facilitate cancer screening and consequently increase cancer-screening rates in federally qualified health centers. Our goal is to promote strategies and research-tested interventions that physicians and other healthcare workers can implement to overcome barriers and make cancer screening more efficient and cost-effective. This article reports on the development efforts required to create an e-learning tool designed for practitioners in community health centers. It concludes that the computer graphics and animation community has an important role in helping healthcare researchers design higher-fidelity educational games and simulations for improving the delivery of chronic care to the millions requiring it.     

A SAS macro for parametric and semiparametric mixture cure models

Cure models have been developed to analyze failure time data with a cured fraction. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. Mixture cure models assume that the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals that will never experience it. The aim of this paper is to propose a SAS macro to estimate parametric and semiparametric mixture cure models with covariates. The cure fraction can be modelled by various binary regression models. Parametric and semiparametric models can be used to model the survival of uncured individuals. The maximization of the likelihood function is performed using SAS PROC NLMIXED for parametric models and through an EM algorithm for the Cox's proportional hazards mixture cure model. Indications and limitations of the proposed macro are discussed and an example in the field of cancer clinical trials is shown.     

A model to predict the survivability of cancer comorbidity through ensemble learning approach

Cancer is one of the most common death causes worldwide. Breast and genital cancers in women and prostate cancer in men constitute three of the most common cancers. Detection and prevention of these types of cancers are critical objectives. Recent findings indicate that some patients suffer from cancer comorbidity. The probability of survival among patients with comorbid condition is lower than those with only one type of cancer. The importance of concomitant chronic illnesses during cancer treatment through the SEER data is assessed through many machine‐learning approaches. In order to improve the accuracy of prediction of survival rates in patients with cancer and comorbidity of cancers, the gradient boosting ensemble method is adopted for feature selection and modelling. This proposed method increases the accuracy rate and reduces the error rate, and exhibits a significant predictive improvement of survival rates in comorbid cancer compared with the previous proposed models.     

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.     

Test of a quick-don wet immersion suit for off-shore use

Tests of “Spacetime” Survival Coverall suits and the associated gloves and boots of the ensemble were carried out on six men. Four men maintained their rectal temperature above 36°C for six hours in a bath of circulating water at 12°C. Two men had to be withdrawn from the bath because they reached a rectal temperature of 35.5°C, the ethical withdrawal criterion, before the six-hour period had elapsed. Linear regression of rectal temperature versus time during the terminal part of the exposure was carried out for all men. One of the subjects who was withdrawn would almost certainly have had a rectal temperature above 34°C at the end of six hours had he remained in the bath. The other subject would have had a rectal temperature classifiable as “incipient death” (30°C). The relatively rapid fall in rectal temperatures in two subjects did not appear to relate directly to body size, but leakage may have been a contributing factor. Comparison with data in the literature indicates that the immersion suit is advantageous, but separate experimentation would have to be carried out to determine quantitatively how valuable it is in extending the survival time of men immersed in cold water in comparison with other suits on the market.     

Medical Data Mining on the Internet: Research on a Cancer Information System

This paper discusses several data mining algorithms and techniques thatwe have developed at the University of Arizona Artificial Intelligence Lab.We have implemented these algorithms and techniques into severalprototypes, one of which focuses on medical information developed incooperation with the National Cancer Institute (NCI) and the University ofIllinois at Urbana-Champaign. We propose an architecture for medicalknowledge information systems that will permit data mining across severalmedical information sources and discuss a suite of data mining tools that weare developing to assist NCI in improving public access to and use of theirexisting vast cancer information collections.     

基于甲骨文数据库管理系统的化学结构检索数据库的设计与实现

设计了一种基于大型数据库管理系统Oracle的化学结构数据存储模式,并实现了相应于此模式的高效化学结构检索方法。结构检索方法建立在图子图匹配算法VF2的基础上,对其进行了必要的改造和扩展,使之适合于化学结构检索。在此基础上,针对美国NCl(National Cancer Institute)25万个化合物的2D结构建立了数据库,成功进行了结构检索试验。结果表明,这种实现方法不仅能高效存储并准确检索化合物的结构信息,而且也容易实现与药物研发过程中所产生的大量其它数据(如生物筛选数据和DNA芯片基因表达数据等)进行高效整合。这个设计的改进版已经集成于微芯公司的药物研发生化信息学软件系统——TASS(Target Activity Structure System)中。     

Simulating Coxian phase-type distributions for patient survival

Coxian phase-type distributions are a special type of Markov model that can be used to represent survival times in terms of phases through which an individual may progress until they eventually leave the system completely. Previous research has considered the Coxian phase-type distribution to be ideal in representing patient survival in hospital. However, problems exist in fitting the distributions. This paper investigates the problems that arise with the fitting process by simulating various Coxian phase-type models for the representation of patient survival and examining the estimated parameter values and eigenvalues obtained. The results indicate that numerical methods previously used for fitting the model parameters do not always converge. An alternative technique is therefore considered. All methods are influenced by the choice of initial parameter values. The investigation uses a data set of 1439 elderly patients and models their survival time, the length of time they spend in a UK hospital.     

Using the Model Coupling Toolkit to couple earth system models

Continued advances in computational resources are providing the opportunity to operate more sophisticated numerical models. Additionally, there is an increasing demand for multidisciplinary studies that include interactions between different physical processes. Therefore there is a strong desire to develop coupled modeling systems that utilize existing models and allow efficient data exchange and model control. The basic system would entail model “1” running on “M” processors and model “2” running on “N” processors, with efficient exchange of model fields at predetermined synchronization intervals. Here we demonstrate two coupled systems: the coupling of the ocean circulation model Regional Ocean Modeling System (ROMS) to the surface wave model Simulating WAves Nearshore (SWAN), and the coupling of ROMS to the atmospheric model Coupled Ocean Atmosphere Prediction System (COAMPS). Both coupled systems use the Model Coupling Toolkit (MCT) as a mechanism for operation control and inter-model distributed memory transfer of model variables. In this paper we describe requirements and other options for model coupling, explain the MCT library, ROMS, SWAN and COAMPS models, methods for grid decomposition and sparse matrix interpolation, and provide an example from each coupled system. Methods presented in this paper are clearly applicable for coupling of other types of models.     

Powered domestic lawnmowers: design for safety

The paper describes a detailed accident investigation carried out by the Institute for Consumer Ergonomics for the Consumer Safety Unit at the Department of Trade. As such it serves to illustrate the application of two specific research techniques (i) analysis of product related accident data, and (ii) ergonomics evaluation of current models - and shows how these may be used to help in defining standards and criteria for the design of safer products. The study identified lawnmower features and activities associated with accidents recorded by the Home Accident Surveillance System. Ergonomics appraisal by expert assessment and user trials highlighted hazards associated with currently available powered lawnmowers. Performance criteria for safer design of selected features were developed with the aim of overcoming these hazards. At the end of the study liaison was sought with manufacturers to discuss how the results from the work could be used to effect.     

数字视频监控系统研究与实现

文章深入分析了数字视频监控系统中的关键技术,并给出一个基于MPEG-4和实时媒体传输技术的系统设计方案,能够满足视频监控系统的需求.在视频采集过程中采用双缓冲技术加速视频流的采集.     

Cure models to estimate time until hospitalization due to COVID-19

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included. The main nonparametric and semiparametric approaches were applied to a real time dataset of COVID-19 patients from the first weeks of the epidemic in Galicia (NW Spain). The aim is to model the elapsed time from diagnosis to hospital admission. The main conclusions, as well as the limitations of both the cure models and the dataset, are presented, illustrating the usefulness of cure models in this kind of studies, where the influence of age and sex on the time to hospital admission is shown.

     

数字视频监控系统研究与实现

文章深入分析了数字视频监控系统中的关键技术,并给出一个基于MPEG-4和实时媒体传输技术的系统设计方案,能够满足视频监控系统的需求。在视频采集过程中采用双缓冲技术加速视频流的采集。     

Survival and success of family‐owned small businesses after hurricane Katrina: Impact of disaster assistance and adaptive capacity

The study's purpose is to examine the relative importance of direct disaster assistance to family‐owned small firms on their survival and success while considering the components of their adaptive capacity after experiencing Hurricane Katrina. This study employed data from the 2013 and 2015 Small Business Survival and Demise after a Natural Disaster Project (SBSD), a project funded by the National Science Foundation. The logistic regression results show that family businesses with SBA loans (or loan guarantees) were significantly more likely to survive. In addition, family businesses where household and family issues were more frequently in conflict with work demands were more likely to survive. On the other hand, the OLS regression results indicate that family businesses with more stress and more family–business conflict were less likely to succeed.     

Proofs of a set of hybrid let-polymorphic type inference algorithms

We present a generalized let-polymorphic type inference algorithm, prove that any of its instances is sound and complete with respect to the Hindley/Milner let-polymorphic type system, and find a condition on two instance algorithms so that one algorithm should find type errors earlier than the other. By instantiating the generalized algorithm with different parameters, we can obtain not only the two opposite algorithms (the bottom-up standard algorithmW and the top-down algorithmM) but also other hybrid algorithms which are used in real compilers. Such instances’ soudness and completeness follow automatically, and their relative earliness in detecting type-errors is determined by checking a simple condition. The set of instances of the generalized algorithm is a superset of those used in the two most popular ML compilers: SML/NJ and OCaml. This work is supported by Creative Research Initiatives of the Korean Ministry of Science and Technology National Creative Research Initiative Center, http://ropas.kaist.ac.kr Work done while the third author was associated with Korea Advanced Institute of Science and Technology Hyunjun Eo: He is a Ph.D. candidate in the Department of Computer Science at KAIST (Korea Advanced Institute of Science and Technology). He recieved his bachelor’s degree and master’s degree in Computer Science from KAIST in 1996 and 1998, respectively. His research interest has been on static program analysis, fixpoint iteration algorithm and higher-order and typed languages. From fall 1998, he has been a research assistant of the National Creative Research Initiative Center for Research on Program Analysis System. He is currently working on developing a tool for automatic generation of program analyzer. Oukseh Lee: He is a Ph.D. candidate in the Department of Computer Science at KAIST (Korea Advanced Institute of Science and Technology). He received his bachelor’s and master’s degree in Computer Science from KAIST in 1995 and 1997, respectively. His research interest has been on static program analysis, type system, program language implementation, higher-order and typed languages, and program verification. From 1998, he has been a research assistant of the National Creative Research Initiative Center for Research on Program Analysis System. He is currently working on compile-time analyses and verification for the memory behavior of programs. Kwangkeun Yi, Ph.D.: His research interest has been on semanticbased program analysis and systems application of language technologies. After his Ph.D. from University of Illinois at Urbana-Champaign he joined the Software Principles Research Department at Bell Laboratories, where he worked on various static analysis approaches for higher-order and typed programming languages. For 1995 to 2003 he was a faculty member in the Department of Computer Science, Korea Advanced Institute of Science and Technology. Since fall 2003, he has been a faculty member in the School of Computer Science and Engineering, Seoul National University.     

视频监控中心数据传输控制的设计与实现

本文利用Win32的多线程编程技术和Windows Socket网络编程技术给出了视频监控系统控制中心数据收发模块的一个解决方案．并将其成功的运用到一个视频监控系统中。