应用马尔可夫预测模型对门诊就诊人次的预测

目的探索马尔可夫决策预测方法在医院信息管理领域的应用,推算和展望在今后一个时期内医生和患者之间合理配置的规律性。方法本文以医学院附属医院1998～2004年七年的门诊就诊人次数据为基础,利用马尔可夫预测的状态转移概率矩阵进行模拟预测。结果该院的状态转移概率表明马尔可夫决策预测方法可以对医院门诊就诊人次进行短期的趋势预测。结论马尔可夫预测方法可以提高工作的预见性与主动性,能够为医院管理者的未来决策提供科学依据,对医院人员的编制及床位的调配等方面的工作具有指导意义。     

马尔可夫链模型在医学高等教育教学质量评价中的应用研究

文章首先介绍了医学高等教育教学质量评价中的马尔可夫链模型基本原理,基于医学高等教育的教学过程带有诸多随机因素的特点,运用马尔可夫链建立相应的动态的随机过程模型进行分析和质量评价。通过流行病与卫生统计学学科的教学质量评价的实例检验,证明了将马尔可夫链模型应用到医学高等教育的教学质量评价中的科学性和可行性。最后总结了该方法的优缺点并提出了对其进行改进的思路。     

一种基于程序状态的随机测试模型

讨论了马尔可夫链模型在随机程序测试中的应用。在建立了程序的马尔可夫链测试模型和马尔可夫链使用模型的基础上,提出了衡量测试完备性的量化计算公式和估计程序平均失败频率的计算公式。测试结果证实了其可行性和正确性。     

基于灰色马尔可夫链模型的数字化放射设备维修工作量的预测

本文阐述了灰色马尔可夫链模型原理,将灰色GM(1,1)预测模型和马尔可夫链状态转移相结合,通过对我院放射科维修工作量预测的实证分析,说明灰色马尔可夫链模型对于具有一定波动性和随机性的数字化放射设备的维修工作量有一定的预测意义.     

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。     

基于马尔可夫随机场的肝实质分割

介绍了马尔可夫随机场（markov random field,MRF）的基本理论,以及基于MRF的图像分割模型及其求解过程。利用MRF分割方法对肝脏CT图片进行了分割,实验结果表明：该方法能够有效对肝脏实质进行分割,在一些模糊区域有更好的分割效果,可用于CT图像序列中的肝实质自动分割。     

Markov模型在卫生领域中应用简介

俄国著名数学家马尔可夫于1906—1912年间提出了一种能用数学分析方法研究自然过程的一般图式-马尔可夫链（Markov chain）。他的研究方法和重要发现推动了概率论的发展，特别是促进了概率论新分支-随机过程理论的发展。随机过程又称马尔可夫过程（Markov process），在建立之初，被用来描述和预测煤气分子在一个密闭容器中的状态。     

卫勤循证决策研究现状与发展

卫勤循证决策是提升多样化军事行动卫勤指挥与保障能力的重要途径,也是保证军队卫勤决策系统性、科学性、反应性和持续性的重要方法体系.本文在界定卫生循证决策和卫勤循证决策基本内涵的基础上,分析了国内外卫生循证决策的主要做法,重点介绍了我军卫勤循证决策研究的现状,并对军队卫勤循证决策研究的发展趋势进行了前瞻性分析.     

医学高等教育的教学质量评价方法研究

文章介绍了医学高等教育教学质量评价的意义、研究现状及前景,在此基础上总结了三种主要的医学教学质量评价方法：层次分析法、模糊综合评判法、齐次马尔可夫链模型,通过对比分析了三种方法在医学院校教学质量评价中的应用,并进一步总结了他们的特点及不足之处,给出相应的改进方法。     

基于马尔可夫决策过程的云平台资源调度

云计算平台可以动态地配置资源,适合基于工作流的科学计算。当前云平台的资源调度研究更多考虑运行时长和成本的最优化,而较少提到鲁棒性。本文提出了一种基于马尔可夫决策过程理论的资源调度算法,对工作流任务进行分组,按照任务的计算量和依赖关系将任务期限分配给各个任务组,在满足工作流总期限的基础上,将异构环境中的云资源分配给工作流的各个任务,通过最大化每个任务组的容忍时间使得整个工作流的鲁棒性达到最优。实验结果表明：该调度算法在异构环境中可以在任务期限和开销内提高调度的鲁棒性。     

Decision trees: an overview and their use in medicine

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.     

The Art of Building Decision Trees

Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable, and quick response. One of the most viable among models are decision trees, already successfully used for many medical decision-making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision support models using four different approaches. We took statistical analysis, a MtDeciT, in our laboratory developed tool for building decision trees with a classical method, the well-known C5.0 tool and a self-adapting evolutionary decision support model that uses evolutionary principles for the induction of decision trees. Several solutions were evolved for the classification of metabolic and respiratory acidosis (MRA). A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.Art (from Latin ars meaning skill) is the skill in doing or performing that is attained by study, practice, or observation     

Accuracy Enhancement in a Fuzzy Expert Decision Making System Through Appropriate Determination of Membership Functions and Its Application in a Medical Diagnostic Decision Making System

The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. It has also been ascertained that there exists only one rule in the rule base whose associated mapping for the ith linguistic variable maps to the same value as the maximum value of the membership function for the ith linguistic variable. The improvement in decision making accuracy was successfully verified in a medical diagnostic decision making system for renal diagnostic applications. Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.     

Cost-Effectiveness Evaluation of EHR: Simulation of an Abdominal Aortic Aneurysm in the Emergency Department

Health care organizations have installed electronic systems to increase efficiency in health care. Empirically assessing the cost-effectiveness of technologies to the health care system is a challenging and complex task. This study examined cost-effectiveness of additional clinical information supplied via an EHR system by simulating a case of abdominal aortic aneurysm devised and acted professionally by the Israel Center of Medical Simulation. We conducted a simulation-based study on physicians who were asked to treat a simulated patient for the prevalent medical scenario of hip and leg pain that actually corresponded to an abdominal aortic aneurysm. Half of the participating physicians from the Department of Emergency Medicine at Tel-Hashomer Hospital – Israel’s largest - had access to an EHR system that integrates medical data from multiple health providers (community and hospitals) in addition to the local health record, and half did not. To model medical decision making, the results of the simulation were combined with a Markov Model within a decision tree. Cost-effectiveness was analyzed by comparing the effects of the admission/discharge decision in units of quality adjusted life years (QALYs) to the estimated costs. The results showed that using EHR in the ED increases the QALY of the patient and improves medical decision-making. The expenditure per patient for one QALY unit as a result of using the EHR was $1229, which is very cost-effective according to many accepted threshold values (less than all these values). Thus, using the EHR contributes to making a cost-effective decision in this specific but prevalent case.     

急诊医学专业住院医师应用临床决策方法的探索与研究

目的：探讨急诊住院医师临床实践中常用的4种临床决策方法（即模式识别法、假设演绎法、事件驱动法、运用规则法）的应用现状,了解其对临床决策方法掌握的情况及影响因素。方法根据住院医师的不同年制把207名住院医师（收集每个住院医师诊疗过的5例患者）收集的1035例患者分3组,比较不同年制医生的临床决策方法及其诊断正确率和影响因素。结果不同年制医生的决策方法差异有统计学意义（P＜0．05）,不同决策方法的诊断正确率差异有统计学意义（P＜0．05）,防御行为和不同决策方法差异有统计学意义（P＜0．05）。不同决策方法的多分类Logistic回归分析显示,住院医师年制、住院医师学历、住院医师接触临床时间、住院患者症状、防御行为、问诊可靠程度,差异有统计学意义（P＜0．05）。结论住院医师最常用的临床决策方法为运用规则法;住院医师年制、住院医师学历、住院医师接触临床时间、住院患者症状、防御行为、问诊可靠程度是住院医师在急诊医学专业临床实践中应用临床决策方法的影响因素。     

The role of three-dimensional printed cardiac models in the management of complex congenital heart diseases

BackgroundThree-dimensional printing is a process enabling computer-assisted conversion of imaging data from patients into physical “printed” replicas. This has been extrapolated to reconstructing patient-specific cardiac models in congenital heart diseases. The aim of this study was to analyze the impact of three-dimensional printing in surgical decision making in selected cases of complex congenital heart disease by creating patient-specific printed models.MethodsPatients with complex congenital heart diseases with unresolved management decisions after evaluation by echocardiography, cardiac catheterization, and cardiac computed tomography were included with intent to aid in surgical decision making. Three-dimensional models were created from computed tomographic images by an outsourced firm using computer applications. All cases were reviewed by the same team before and after the cardiac models were prepared. The management decisions were grouped as either “corrective surgery” or “no surgery or palliation” The impact of the surgical decision pre and post three-dimensional cardiac model was analyzed by applying Cohen’s kappa test of agreement.ResultsTen patients were included, of which five were of increased pulmonary blood flow, and five were of decreased pulmonary flow. The commonest indication for three-dimensional printed models was to establish the routability of the aorta and pulmonary artery to their respective ventricles (in five patients). The nonagreement between the decision taken before and after the cardiac model was 80%, with kappa −0.37 and P value 0.98.ConclusionsThree-dimensional printed cardiac models contribute to better decision making in complex congenital heart diseases enabling safer execution of any complex congenital heart surgery.     

延安地区被误诊哮喘患儿发病危险因素的变化探讨

目的分析12年来延安地区被误诊的哮喘患儿的发病危险因素的变化情况,为本区儿童哮喘的诊断及治疗提供参考。方法选择2000年1月至2011年12月在延安大学附属医院确诊被误诊的哮喘患儿148例,选择148名年龄和性别匹配的健康儿童为对照。利用Logistic回归分析查找被误诊的哮喘患儿的发病相关危险因素。结果 2000年1月至2003年12月被误诊的哮喘患儿的发病主要影响因素为细菌感染、哮喘家族史、个人过敏史、气候变化。2004年1月至2007年12月被误诊的哮喘患儿的发病主要影响因素为环境因素、细菌、病毒混合感染、哮喘家族史以及个人过敏史。2008年1月至2011年12月被误诊的哮喘患儿的发病主要影响因素包括病毒感染、支原体感染、肥胖、环境因素、哮喘家族史、个人过敏史。结论被误诊的哮喘患儿的发病危险因素逐渐变化,病毒、支原体感染以及单纯性肥胖逐渐成为诱发儿童哮喘的新的主要危险因素。     

支气管哮喘动物模型的研究进展

建立支气管哮喘动物模型有助于对哮喘的研究。针对Th2型细胞驱动的变态反应动物模型研究较多,并已应用于相关药物的研究。对支气管哮喘动物模型有一定疗效的药物对人类患者疗效欠佳,这可能与选择实验的动物和诱导哮喘表型的方法有关。啮齿动物和豚鼠模型是最常使用的支气管哮喘动物模型。目前动物模型仍存在许多问题,如在免疫学和解剖学上动物与人类存在差异,大多数模型在致敏时需要佐剂,成年动物应用较多。因此,明确每种模型的相对优点和局限性十分必要。     

当前国内外医疗决策支持系统研究热点的文献计量学分析

摘要：[目的]为了解医疗决策支持系统领域的研究现状并预测未来发展方向,为相关学者选择研究方向时提供参考和依据。[方法]以PubMed数据库中文献记录为样本,用医学主题词（Mesh）制定检索策略进行检索,获取关于医疗决策支持系统研究主题的相关文献,以XML格式下载存储其书目信息,并用BICOMB软件对文献中主要主题词进行频次统计并截取高频词,进一步形成高频主题词-论文矩阵。根据高频主题词在论文中的共现情况利用gCluto软件对其进行聚类分析,根据高频主题词聚类分析结果和对应的类标签文献,分析当前医疗决策支持系统领域的研究方向。[结果]总结出当前医疗决策支持系统的研究热点有两个方面,分为临床决策支持系统具体构建技术的研究和临床决策支持系统社会学相关问题的研究。[结论]医疗决策支持系统是一个新兴并值得重点深入关注的领域,以BICOMB和gCluto软件为基础的文献计量学方法研究,能够给临床医学以及临床信息管理领域的研究者提供全面信息及热点参考。