专家系统在医学上的研究现状及进展

专家系统在医学上的研究已有超过50年的历史,在一些领域已取得较大进展。本文从知识获取、表示、推理三方面论述了其研究现状及进展。     

Bayesian statistics in medical research: an intuitive alternative to conventional data analysis

Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a 'prior distribution' that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying 'no effect'. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).     

模块化模糊诊断方法在中医辨证分型中的研究

中医学面临在继承发扬传统的基础上探求现代化转变,中医诊断的科学化、规范化、标准化、定量化和精确性等问题变得更为迫切。本文提出一种基于模糊数学和模块化思维的中医疾病诊断方法,以期在系统整理古今资料及专家经验的基础上,尝试模拟中医辨证诊治过程建立模块化疾病诊治方法。该方法适用于典型疾病的中医辨证诊断,对于中医专家的经验积累和知识共享都具有实际应用价值。     

基于空间信息及灰度信息的塔型模糊聚类图像分割

对于二维灰度图像,基于灰度信息的图像信息熵分析法,由于没有考虑到图像的空间信息,存在着对图像信息描述不准确的问题。在进行图像分割时,若图像复杂到一定程度时,无法得到满意的效果。本文从空间信息和灰度信息出发,将图像的空间信息和灰度信息作为特征向量,对比较复杂的图像进行聚类分割,取得了满意的效果。经分析和实验验证,本文提出的基于空间信息和灰度信息的塔型模糊C-均值聚类（PFCM）图像分割方法与传统的非塔型聚类算法相比,具有收敛速度快、稳定性好等优点。同时,由于对初始聚类中心的合理初始化,消除了聚类分析中常见的死点问题。     

Estimating efficacy in clinical trials with clustered binary responses

When non-compliance occurs in a clinical trial, it may be of interest to supplement the intent-to-treat analysis with an analysis of the efficacy (or biological effect) of therapy. Sommer and Zeger (1991) developed a method for estimating efficacy applicable to the case of a binary response variable and all-or-none compliance that assumes independent subject responses. We extend this approach to accommodate within-cluster correlations as may be expected in a cluster-randomized design. The method is illustrated using data from a controlled village-randomized clinical trial conducted in Indonesia to investigate the effect of vitamin A supplementation on mortality in children. We find that within-cluster correlations for these data are very small and that taking into account the clustering does not substantially affect inferences in this case. Additional calculations show that small within-cluster correlations (though larger than those found in the vitamin A data) may have a large impact on efficacy inferences. We also present the results of a simulation study that demonstrates the validness of the proposed approach for finite sample sizes.     

Analysis of ulcer data using hierarchical generalized linear models

In multi-centre clinical trials, heterogeneities in individual hospital treatment effects can be modelled as random effects. Estimates of the individual hospital treatment effects and estimate of the mean treatment effect, allowing for the presence of overall hospital differences, are required, together with some measure of their uncertainty. Systematic inferences from the hierarchical-likelihood are now possible, using hierarchical generalized linear models. We show how to construct profile likelihoods for the treatment effects of individual hospitals.     

The application of GIS technology to equitably distribute fieldworker workload in a large, rural South African health survey

A method is presented that has numerous applications to health systems provision in developing countries where limited physical access to primary health care is a major factor contributing to the poor health of populations. An accessibility model within a geographical information system (GIS) is used to predict average inter-homestead walking times and subdivide the study area into units of equal completion time. The method could be used to ergonomically design home-based care and tuberculosis directly observed treatment programmes and inform the siting of health facilities. The paper highlights the use of GIS technology as a powerful tool in developing countries.     

基于广义模糊吉波斯随机场的噪声图象分割

基于吉波斯随机场分割模型的图象分割方法是一种常用的重要方法。本文结合广义模糊集理论，针对噪声大的模糊图象分割问题，重新定义了吉波斯场的集团势函数，将广义模糊隶属度引入势函数，建立了新的分割模型。在此基础上用条件迭代模式（ICM）法对图象进行了优化分割。实验表明，该方法能有效地分割退化的模糊图象。     

模糊马尔可夫场模型与图像分割新算法

本文建立模糊马尔可夫场模型，并提出基于模糊马尔可夫场的图像分割新算法。该算法同时处理模糊性和随机性，因此能有效获取图像的先验知识。在模糊马尔可夫场与待分割图像之间用经典的马尔可夫场关联。模糊马尔可夫场是经典马尔可夫场的推广，当模糊马尔可夫场失去模糊性时，它将退化为经典的马尔可夫场。给定图像，随即进行模糊化处理；以最大后验概率作为优化准则修正模糊马尔可夫场的隶属度；最后按照最大隶属度原则消除模糊性，从而得到图像的分割。该算法可以有效地虑除噪和消除部分容积效应，得到更为准确的分割结果。     

高校教学评价系统中模糊矩阵综合评判模型的建立

简要地介绍模糊矩阵,以科学可行的评价指标为依托,提出了一种新的评价方法,建立综合评判模型并计算出量化的