CN1613088A - 用于心脏病筛选的病人数据挖掘 - Google Patents

用于心脏病筛选的病人数据挖掘 Download PDF

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CN1613088A
CN1613088A CNA028266900A CN02826690A CN1613088A CN 1613088 A CN1613088 A CN 1613088A CN A028266900 A CNA028266900 A CN A028266900A CN 02826690 A CN02826690 A CN 02826690A CN 1613088 A CN1613088 A CN 1613088A
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B·R·劳
S·克里斯南
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Abstract

提供了一种用于筛选冠心病的系统和方法。所述方法包括以下步骤：检索一个用于估计冠心病风险的测试，所述测试包括多个与冠状风险因素有关的数据域；访问数据库以便用个别病人的信息填充所述数据域；以及计算所述个别病人患上冠心病的风险估计。一种系统包括：第一数据库，它包括多个结构化的计算机病历；第二数据库，包括关于冠心病的知识库，所述第二数据库包括至少一个用于确定冠心病风险的测试；以及一个处理器，用于从所述第二数据库中检索所述至少一个测试，用从所述第一数据库检索到的病人信息来填充所述至少一个测试，以及为至少一个病人计算风险估计。

Description

用于心脏病筛选的病人数据挖掘
相关申请的交叉参考
本申请要求2001年11月2日提交的序号为60/335,542的美国临时申请的利益，该申请在此全部引入作为参考。
技术领域
本发明涉及医疗信息处理系统，更具体地，本发明涉及一种计算机化的系统和方法，用于筛选冠心病(CHD)病人、估计一个人患上CHD的风险因素以及管理患CHD的人。
背景技术
冠心病在西方社会是头号杀手。通过尽可能早地检查冠心病，可以进行适当、有效以及节省成本的治疗。
然而，心脏病专家面临着来自各种不同来源的日益增多的数据：显象方式、病人报告、ECG跟踪等。随着信息来源数量的扩展，手动地提取和吸收所有可用的数据以及评估各种治疗选择变得越来越困难。此外，随着管理医疗的推动，希望心脏病专家在相同的时间量中治疗和管理更多的病人。
当前，相当大量的证据证实心血管风险和疾病没有得到妥善的治疗。解释这种现象的因素包括知识的缺陷、对治疗期间包括类脂物的目标水平的建议的混乱、医生和病人之间的不良沟通以及医生理解和指导应用中的变化。
综上所述，需要一种改进的系统和方法，用于筛选患冠心病的人、估计个别病人患上冠心病的风险以及管理患冠心病的病人。
发明内容
提供了一种系统和方法，用于筛选、检查和管理患冠心病(CHD)的病人。
根据本发明的一个方面，提供一种用于筛选冠心病的方法，所述方法包括以下步骤：检索一个用于估计冠心病风险的测试，所述测试包括多个与冠状风险因素有关的数据域；访问数据库以便用个别病人的病人信息填充所述测试的数据域，所述数据库包括计算机病历；以及计算个别病人患上冠心病的风险估计。所述方法还包括以下步骤：对于同来自结构化和非结构化数据源的冠状风险因素有关的信息进行数据挖掘；以及将这些信息编辑为结构化的计算机病历。
根据本发明的另一个方面，冠心病筛选系统包括：第一数据库，包括多个结构化的计算机病历；第二数据库，包括关于冠心病的知识库，所述第二数据库包括至少一个用于确定冠心病风险的测试，其中所述至少一个测试包括多个关于冠状风险因素的数据域；以及一个处理器，所述处理器用于从所述第二数据库中检索所述至少一个测试，用从所述第一数据库检索到的病人信息填充所述至少一个测试的数据域，以及为至少一个病人计算风险估计。通过对来自结构化和非结构化的数据源的关于冠状风险因素的信息进行数据挖掘来编辑所述第一数据库。
根据本发明的又一个方面，提供一种机器可读的程序存储设备，所述程序存储设备确实包含机器可执行的、实现用于筛选冠心病的方法步骤的指令程序。所述方法步骤包括检索用于估计冠心病风险的测试，所述测试包括多个关于冠状风险因素的数据域；访问数据库以便用个别病人的病人信息填充所述测试的数据域，所述数据库包括计算机病历；以及计算个别病人患上冠心病的风险估计。
附图说明
结合附图从下面详细描述中，本发明的上述和其他方面、特征和优点将变得更加显而易见，在附图中：
图1是根据本发明实施例本发明适用的计算机处理系统的框图；
图2说明根据本发明实施例的示例性的冠心病筛选系统；以及
图3说明根据本发明实施例筛选、监视和管理病人的流程图。
具体实施方式
为了方便对本发明的清楚理解，在此提供说明性的实例，这些实例描述本发明的某些方面。然而，可以理解的是这些说明并不意味着限制本发明的范围，而在此提供为了说明与本发明相关的某些概念。
提供了一种用于筛选、检查和管理冠心病(CHD)病人的系统和方法。根据本发明的实施例，基于计算机的冠心病筛选系统将辅助医生估计和管理冠心病。首先，该系统将吸收来自计算机病历(CPR)中的成象和非成象源的信息。可以以有意义的方式自动地提取、组合以及分析这些数据，并将结果提供给医生。这样的系统还将帮助避免错误，以及向无经验者提供从专家用户基于感兴趣的疾病的领域知识库以及已建立的临床指导“捕获到的”知识。在每个特定的诊断测试中，该系统将参与自动提取信息，导致工作流程的潜在改进以及在评价这些结果时提供强有力的“第二读者”。在评价之后，该系统还将根据临床指导提供建议的治疗和后续行动。最后，该系统可以随着时间跟踪病人，从而估计疾病的进展和治疗的功效。
在冠状动脉疾病领域，人类可以分成两个组：那些已知或怀疑患有冠状动脉疾病的人以及那些没有冠状动脉疾病的人。在后一种情况中，关键在于促进预防以及减少冠状动脉事件的风险。在此，冠心病筛选系统针对的使用者是临床心脏病专家和一般的开业医生，以便帮助估计、监视和降低冠心病的风险。
在已知或怀疑患有冠心病的人的情况下，计算机辅助冠心病筛选系统的任务稍有不同。首先，这样的系统可以帮助医生估计和诊断该疾病。其次，该系统可以帮助心脏病专家估计疾病的严重性，并且帮助识别可能的治疗。最后，该系统可以帮助估计疾病由于时间或治疗而引起进展或退化。
可以理解的是本发明可以以硬件、软件、固件、专用处理器或它们的组合的各种形式实现。优选地，本发明以如确实包含在程序存储设备上的程序的软件实现。该程序可以加载到包括任何合适的体系结构的机器上，并由其执行。优选地，该机器在计算机平台上实现，该计算机平台具有诸如一个或多个中央处理器(CPU)、随机存取存储器(RAM)和输入/输出(I/O)接口的硬件。该计算机平台还包括操作系统和微指令代码。在此描述的各种过程和功能可以是微指令代码的一部分，或者可以是通过操作系统执行的程序(或其组合)的一部分。此外，各种其他的外围设备可以连接到该计算机平台，例如附加的数据存储设备和打印设备。
可以理解的是因为在附图中描述的一些组成系统部件和方法步骤优选地以软件实现，所述系统部件(或过程步骤)之间的实际连接可以根据本发明被编程的方式而不同。
图1是根据本发明实施例，本发明可以适用的计算机处理系统100的框图。系统100包括至少一个处理器(在下文中为处理器)102，该处理器经由系统总线104可操作地耦合到其他部件。只读存储器(ROM)106、随机存取存储器(RAM)108、I/O接口110、网络接口112和外部存储器114可操作地耦合到系统总线104。各种外围设备，例如显示设备、盘存储设备(例如、磁盘或光盘存储设备)、键盘和鼠标可以通过I/O接口110或网络接口112可操作地耦合到系统总线104。
计算机系统100可以是独立的系统或者经由网络接口112链接到网络。网络接口112可以是硬连线的接口。然而，在各种示例性的实施例中，网络接口112可以包括适于与另一个设备来回传输信息的任何设备，例如通用异步收发器(UART)、并行数字接口、软件接口、或者已知或新近开发的软件和硬件的任何组合。网络接口可以链接到各种类型的网络，包括局域网(LAN)、广域网(WAN)、内部网、虚拟个人网络(VPN)以及因特网。
可以使用由处理器102管理并驻留在例如硬盘的存储器上的数据库管理系统(DBMS)来实现外部存储器114。然而，应该理解的是外部存储器114可以在一个或多个附加的计算机系统上实现。例如，外部存储器114可以包括驻留在单独计算机系统上的数据仓库系统。
本领域的技术人员将会理解在不脱离本发明的精神和范围的情况下，可以使用其他替换的计算环境。
参考图2，说明了根据本发明实施例的示例性的冠心病(CHD)筛选系统200。CHD筛选系统200包括处理多个执行不同任务的模块的处理器202。该处理器耦合到第一数据库204，该第一数据库被编辑以存储关于感兴趣的疾病(在此是冠心病)的结构化的计算机病历(CPR)。
优选地，结构化的数据库204是使用数据挖掘技术用基于人口的病人信息进行填充的，在与此一起提交的序号为10/＿＿，＿＿(代理文件编号为8706-600)的共同待决的美国专利申请“病人数据挖掘(Patient Data Mining)”(Rao等人)中描述了该数据挖掘技术，该美国专利申请在此全部引入作为参考。该专利申请教导用于挖掘高质量结构化的临床信息的数据挖掘框架。该数据挖掘框架包括数据挖掘器206，其具有如REMIND系统中的功能和性能，从西门子医疗解决方案中可以商业获得，该数据挖掘器基于包含在知识库中的特定领域知识从计算机病历(CPR)挖掘医疗信息。CPR可以是结构化的格式208(例如图表、表格、计费信息等)和/或非结构化的格式210(例如医生的指示、诸如MR(磁共振)图象和CT(计算机断层摄影术)扫描、ECG波形的图象等)。特定领域知识可以涉及感兴趣的疾病、医院等。数据挖掘器206包括用于从CPR提取信息、随时间以有原则的方式组合所有可用证据、以及从该组合过程得出结论的部件。挖掘的医疗信息被存储在结构化的CPR数据库中，例如数据库204。
处理器202还耦合到第二数据库212，该第二数据库包括与感兴趣的疾病有关的现有技术信息。该信息可以包括标准过程、已建立的治疗指导、用于估计和诊断的标准化的测试等。
此外，处理器202适于手动地接收输入的病人数据214，其将在第一结构化的数据库204中处理和存储。
CHD筛选系统200与第一结构化的数据库204和医疗知识数据库212相互配合，以便估计病人患上CHD的风险、推荐治疗和生活方式的改变以降低病人的估计风险、以及执行敏感度分析以确定什么样的因素对于病人来说是最大的风险。CHD筛选系统200所执行的每个任务均由一个可执行的模块执行，该可执行的模块驻留在系统202的处理器中和/或该系统的存储器设备(例如RAM、ROM、外部存储器等)中。
参考图2和图3，将与用于估计CHD风险、监视CHD病人以及建议治疗和生活方式改变的方法一起进一步描述CHD筛选系统。
初级和二级CHD预防的目标都是为了减少随后急性冠脉事件的风险，并由此减少死亡率并延长寿命。在初级预防中，想法就是识别处于患上冠状动脉疾病的高风险的健康(无症状)的个人，并且开始治疗和生活方式改变从而降低这种风险。二级预防对于已经经历急性冠脉事件的人来说所做的是相同的。本发明的系统和方法将协助医生并在以下方面中起到重要的作用：风险估计、基于已建立的指导而得到的治疗和生活方式改变的目标建议、以及监视朝向风险降低目标的病人进展。
在初级预防中，识别处于患上冠状动脉疾病的高风险的无症状的个人，并且开始治疗以及推荐生活方式改变来降低这种风险。首先，根据风险因素对无症状病人患冠心病的风险进行估计。例如，国家胆固醇教育计划(NCEP)最近产生一组用于治疗和管理类脂物异常的成人治疗小组(Adult Treatment Panel)(ATP III)指导。在这些指导中，对于没有冠心病史的人在10年期间发生急性冠脉事件的风险是根据Framingham算法或测试的修改版本进行计算的。Framingham算法使用传统的风险因素，例如性别、肥胖、吸烟、总胆固醇、HDL胆固醇、年龄、糖尿病和血压来确定总的风险。ATP III指导还将其他的风险因素，例如家族史和高血压包括在它的风险模型中。除了这些传统的风险因素之外，该指导还认可了新出现的风险因素，例如血半胱氨酸(hemocysteine)、脂蛋白、以及诸如可以指示急性冠脉事件风险的高敏感度C反应蛋白的发炎标志。此外，NCEP ATP III根据初级和二级预防的这些风险因素，已经制订了对于治疗和生活方式改变的特定推荐。这样的测试、推荐和指导将被存储在医疗知识数据库212中。
这些指导用来帮助临床心脏病专家以及病人的初级医疗医生估计风险并且帮助预防急性冠状动脉事件的发生。在这种情况下，CHD筛选系统200可以以各种不同的方式提供有价值的辅助。首先，风险估计需要获得包含在病历内的各种不同位置中的临床信息。通过数据挖掘器206挖掘结构化的208和非结构化的210数据，并将其存储在数据库204中的结构化的CPR中(步骤302)。然后，系统200访问第二数据库212以便检索诸如Framingham算法的测试，从而估计个别病人的风险(步骤304)。估计模块202-1接收该测试，用从结构化的数据库204检索到的信息来填充该测试内的多个数据域，并且计算病人的风险(步骤306)。根据风险估计或分数，将该病人分类为低风险(步骤308)、中等风险(步骤310)或高风险(步骤312)。
可以理解的是不是所有计算风险估计所必需的信息都可以从病历中容易地得到。如果信息丢失，则该系统将会挖掘可用的数据以便作出关于该丢失信息的概率断定。然后该系统利用所述概率信息来计算病人的风险估计。
可以理解的是自动检索信息并计算这些风险的能力会节省医生的时间，并且使得能够自动筛选非常多的人群。不完整或冲突的信息可以引起医生的注意。
一旦为个别病人估计风险，将会自动生成特定的一组针对该病人并基于特定采用的指导的推荐测试、治疗和/或生活方式改变(步骤316)。一旦随后由病人访问筛选，则CHD系统200将会通过病人管理模块202-2跟踪相对于病人表现的推荐。例如，可以将在随后检查中的病人胆固醇水平和生活方式改变与指导目标和先前的记录水平相比，并且为医生自动生成随后的报告。病人管理模块可以周期性地重新估计病人的风险，并且如果测试到显著的改变就警告适当的人员。此外，病人管理模块可以由于估计和/或测试之间的持续时间推荐重新估计的时间。
除了估计当前的风险之外，CHD系统200将通过敏感度模块202-3对各种风险因素执行敏感度分析，以便估计每个单个风险因素对该病人的重要性。也就是说，对于每个风险因素，该系统将会估量其在估计个人的急性冠脉事件的整体风险中各自的重要性。首先，创建一个模型来模拟与正被测试的病人具有相似特性的病人(步骤320)。建模模块202-4可以通过根据存储在医疗知识数据库212中的数据模拟相似的病人，或者可以通过使用感兴趣疾病(即冠心病)的领域知识库、经由数据挖掘器206、从基于人口的数据源挖掘相似病人的数据，来生成该模型；可替换地，该模型可以是上述两者的组合。然后，敏感度模块202-3与建模模块202-4相互配合，通过改变输入数据来模拟不同情况，以确定哪个因素最影响该病人的风险估计(步骤322)。
由于几个原因，敏感度分析是很重要的。首先，对于很多病人来说，通常并不是测试所有的风险因素。例如，其中一个风险因素是糖尿病的存在。然而，不是所有病人都已经进行血糖测试。在这种情况下，首先不考虑这种信息估计心脏病的风险。然后，进行具有不同血糖结果的分析，以便查看风险估计的结果是否改变。如果改变是显著的，则该系统可以推荐进行血糖测试用以细化病人的心脏风险。第二，敏感度分析将会估计病人何时需要回来进行另一次筛选以及应该进行什么样的检查(步骤316)。例如，众所周知，血压在不同次读取之间可以变化很大，它也可以随着时间缓慢地上升或下降。由于知道这种变量的典型可变性，该系统通过估计什么类型的变化可能导致风险估计的显著改变，可以帮助决定何时病人需要回来进行另一次血压读取。
此外，系统200将会推荐是否需要进一步的风险分级。如果初始的风险估计显示一个人具有低的急性冠状动脉事件风险(步骤308)，则医生可以推荐具有饮食和运动的健康生活方式(步骤316)。相反地，如果估计显示了高的急性冠状动脉事件风险(步骤312)，则医生可以根据临床指导选定一些治疗种类(步骤316)，例如，阿司匹林或降低胆固醇的药物。然而，有些人可能显示中等的心脏病风险(步骤310)。在这些情况下，可能需要进一步估计和细化病人的冠心病风险(步骤314)。
在需要进一步风险分级的情况下，可以使用很多种不同的技术，而且可以根据心脏病专家的经验、方便程度和有权使用的设备来选择技术。很多被开发来进一步对风险分级的技术是通过测量动脉硬化负担来进行的，例如(1)测量踝/臂血压指数(ABI)；(2)测量血半胱氨酸、脂蛋白和诸如高敏感度C反应蛋白的发炎标志，以及其他新出现的生化标志；(3)使用高频B模式超声波从颈动脉测量内膜-中层厚度(IMT)；(4)使用电子束计算机断层摄影术(EBCT)估计冠状动脉中的斑块；(5)利用磁共振成像(MRI)估计动脉硬化斑块的成分；(6)估计内皮功能以便确定动脉硬化风险；以及(7)例如使用Agatston分数对冠脉钙评分。
为了方便风险分级，系统200可以进一步包括成像模块202-5，以便(例如通过传统的图象分段方法)从上述的成像源自动提取信息，以及将该提取的信息与先前估计的风险组合起来重新估计该病人的总风险。任何风险分级的结果都可以被用于使用这种附加的风险估计根据已建立的临床指导来生成针对病人的推荐(步骤316)。
实现用于二级预防的CHD系统比用于初级预防的系统更简单。其原因是一旦病人已经有过急性冠脉事件，他们总是处于随后事件的高风险。因此，在这些个人中很少需要风险估计。更确切地，二级预防的主要侧重点是生成特定的一组针对病人并基于特定采用指导的推荐的治疗和生活方式改变。
已经为初级和二级预防建立了生活方式改变以及类脂物管理和血压的目标。通过在时间上跟踪病人(步骤318)，该系统可以自动地估计该病人是否达到期望的风险降低的目标，以及治疗或实施中是否需要进行改变。特定饮食改变、运动、或例如降低胆固醇的药物可以被反馈到该系统中以便重新设计治疗并对个别病人产生新的推荐。按照惯例，跟踪病人需要人工地监视病人信息，并将其与已建立的标准进行比较。这些人工监视技术导致心血管风险的不一致的管理。
而且，CHD系统可以被用于辅助对具有CHD的病人的诊断。通常，病人第一次咨询心脏病专家是在冠状动脉疾病已经显著发展之后，并且病人显示出一些症状。对于这组人，能够快速地诊断该疾病，然后采用适当的治疗并监视他们的进展是很重要的。可以组合所有可用的关于病人的信息来进行诊断，并且使用专利申请“用于诊断和预测病人状态的病人数据挖掘(Patient Data Mining for Diagnosis andProjections of Patient States)”中描述的技术，基于领域知识库对特定病人的问题进行概率推理，上述专利申请是由Rao等人与此一起提交的共同待决的美国专利申请，序号为10/......(代理文件编号为8706-624)，其在此全部引入作为参考。
例如，在急诊室中，病人可能表现为胸痛。急诊室医生必须能够诊断急性冠脉事件，并且可能需要开始治疗以使病人稳定。根据ACC/AHA指导，心电图(ECG)是在病人表现出胸痛、眩晕或晕厥(这些症状可能是猝死或心肌梗塞的预兆)时首先选择的步骤。在ECG无法诊断的情况下，可以使用超声波估计局部心脏收缩的室壁运动异常。因为急诊室医生对解释这些测试可能不像心脏病专家一样有经验，所以CHD系统可以提供项目清单辅助诊断，并且从诸如ECG或超声波检查的来源中自动提取信息以便参与对急性冠脉事件的迅速确定。此外，该系统可以根据已建立的临床指导提供建议的立即治疗。
此外，CHD系统可以帮助临床心脏病专家回答重要的临床问题，包括：诊断梗阻性冠心病、估计疾病的严重性和复杂性、估计患病的心脏组织的成活力、以及根据已建立的临床指导推荐病人管理。
心脏病专家控制很多诊断工具来帮助回答这些问题，例如，心电图、冠状血管造影术、放射性核素造影术、超声波、磁共振成像、电子束计算机断层摄影术等。这些方式中的每一种测量冠状动脉疾病的直接或代用的指标。每个可以单独地帮助提供冠状动脉疾病的证据。对心脏病专家使用的诊断工具的选择通常是根据可用性、经验和舒适水平作出的。每种方式在估计冠状动脉疾病时测量得稍有不同。因此，登记来自不同源的数据以提供估计冠状动脉疾病时更加完整的描述可能是最有力的。当前，通常使用定性或半定量的方法进行冠状动脉疾病的诊断。因此，这种诊断方法的有效性在很大程度上取决于医生的经验和知识。例如，使用目视检查继之以点评分来完成用于估计整体功能和局部异常的负荷超声心动图。
CHD系统以定量的方式从各种不同的来源中提取信息并将其组合，从而帮助临床心脏病专家解决这些临床问题，扩充医生本身的直觉和经验。这样，该系统可以帮助医生在他们自己的决策过程中遵循已接受的指导和实践。
除了检查冠状动脉疾病之外，可以使用很多成像方式来估计疾病随着时间或响应于治疗的进展或退化。其中一些包括超声波、冠状血管造影术、放射性核素造影术和血管内超声波。很多时候，这些技术被用于研究特定治疗的效果，例如血管再造。在另一种情况中，这些技术也可以被用于监视病人随着时间的进展和退化，以便估计何时以及是否必需介入。
CHD系统将从例如由分段、容积显示(volume rendering)等产生的图象中提取信息，并且登记关于病人的来自不同时间点和不同来源的信息，用于估计疾病的进展和退化。通过创建这样的自动系统，医生可以更简单地监视冠状动脉疾病的进展或退化，这种系统可以参与决定一种特殊治疗计划的功效。
在冠状动脉疾病领域，本发明的系统和方法可能在病人的整体管理中起到很大的作用，包括预防、检查、治疗和监视。如今，关于病人的信息来自很宽范围的各种不同来源，包括病人的病例、诸如ECG的波形数据、成像数据、血液测试等。此外，由诸如ACC、AHA和ESC的团体建立了很多临床指导，用于讨论诸如预防、检查和治疗的问题。本发明的系统和方法可以通过从很宽范围的各种不同来源自动收集信息并分析它们来帮助医生。信息可以和基于已建立的临床指导的建议一起提供给医生。
可以理解本发明的各种实施例是在医生工作流程的范围内进行定义的。这些实施例可以以如由临床工作流程和实用性规定的不同子系统内的分布系统的形式存在。例如，某些部件可以在成像方式的范围内，例如在超声波系统上或MRI控制台系统上。其他的部分或部件可以驻留在检查工作站上，例如KinetDx或LeonardoTM工作站。综合的系统可以附属在SorianTM心脏病学系统上。它们一起将形成联合的临床解决方案。可替换地，这样的系统可以作为远程服务器存在，产生ASP(应用服务供应商)模型的解决方案。这允许小的系统，例如手持式超声波系统以及其他的手持式设备(例如，个人数字助理、手持式计算机、膝上型计算机等)，用以在远程地点、在急诊室或在医院之外的出事现场影响CHD系统。
尽管在此参考附图已经描述了本发明的说明性的实施例，但是可以理解的是本发明并不局限于这些明确的实施例，并且在不脱离本发明的范围和精神的情况下，本发明的技术人员可以作出各种其他的改变和修改。

Claims (44)

1.一种用于筛选冠心病的方法，所述方法包括以下步骤：
检索用于估计冠心病风险的测试，所述测试包括关于冠状风险因素的多个数据域；
访问数据库以便用个别病人的病人信息来填充所述测试的数据域，所述数据库包括计算机病历；以及
计算所述个别病人患上冠心病的风险估计。
2.如权利要求1所述的方法，其中所述测试是修改的Framingham算法。
3.如权利要求1所述的方法，其中冠状风险因素包括下列中的一个或多个：性别、肥胖、吸烟、总胆固醇、HDL胆固醇、年龄、糖尿病、血压、家族史、高血压、血半胱氨酸、脂蛋白以及炎性标志物。
4.如权利要求1所述的方法，还包括以下步骤：
对同来自结构化和非结构化数据源的冠状风险因素有关的信息进行数据挖掘；以及
将所述信息编辑成数据库中的结构化的计算机病历。
5.如权利要求1所述的方法，还包括以下步骤：根据所计算的风险估计来确定个别病人患上冠心病的风险是低、中等还是高。
6.如权利要求5所述的方法，其中在确定冠心病的风险之后还包括使用附加的风险因素对风险估计进一步分级的步骤。
7.如权利要求6所述的方法，其中附加的风险因素测量个别病人的动脉硬化负担。
8.如权利要求7所述的方法，其中动脉硬化负担的测量包括测量踝/臂血压指数(ABI)、测量内膜-中层厚度(IMT)、估计冠状动脉中的斑块、估计动脉硬化斑块的成分、估计内皮功能以及对冠脉钙评分。
9.如权利要求1所述的方法，还包括以下步骤：根据所计算的风险估计向个别病人推荐生活方式改变。
10.如权利要求9所述的方法，还包括以下步骤：跟踪病人信息一段时期，以及重新计算所述个别病人的风险估计。
11.如权利要求1所述的方法，还包括以下步骤：根据所计算的风险估计向个别病人推荐治疗。
12.如权利要求11所述的方法，还包括以下步骤：跟踪病人信息一段时期，以及重新计算所述个别病人的风险估计。
13.如权利要求1所述的方法，还包括以下步骤：对个别病人的冠状风险因素执行敏感度分析，以便确定哪个因素对于影响风险估计最有效果。
14.如权利要求13所述的方法，还包括以下步骤：创建冠心病病人的模型，以便模拟不同变量的效果从而执行敏感度分析。
15.如权利要求14所述的方法，其中所述模型基于感兴趣的疾病的医疗知识。
16.如权利要求15所述的方法，其中所述模型基于从表现冠心病的病人的基于人口的数据源中挖掘的信息。
17.如权利要求1所述的方法，还包括根据所计算的估计确定后继估计的时间的步骤。
18.如权利要求1所述的方法，还包括以下步骤：
确定是否所述多个数据域中的至少一个数据域没有被填充；以及
推荐要被实施的测试以便填充所述至少一个数据域。
19.如权利要求1所述的方法，还包括以下步骤：
确定是否所述多个数据域中的至少一个数据域没有被填充；以及
用概率方法估计所述数据域的信息。
20.如权利要求13所述的方法，还包括以下步骤：根据所确定的风险因素来确定后继估计的时间。
21.如权利要求13所述的方法，还包括以下步骤：
确定是否所述多个数据域中的至少一个数据域没有被填充；以及
如果所述至少一个数据域是所确定的风险因素，则推荐要被实施的测试。
22.一种冠心病筛选系统，包括：
第一数据库，包括多个结构化的计算机病历；
第二数据库，包括关于冠心病的知识库，所述第二数据库包括至少一个用于确定冠心病风险的测试，其中所述至少一个测试包括关于冠状风险因素的多个数据域；以及
一个处理器，用于从所述第二数据库检索所述至少一个测试，用从所述第一数据库检索的病人信息来填充所述至少一个测试的数据域，并且为至少一个病人计算风险估计。
23.如权利要求22所述的系统，其中通过对来自结构化和非结构化的数据源的关于冠状风险因素的信息进行数据挖掘来编辑所述第一数据库。
24.如权利要求22所述的系统，还包括估计模块，所述模块用于根据计算的风险估计来确定所述至少一个病人患上冠心病的风险是低、中等还是高。
25.如权利要求24所述的系统，其中如果所述个别病人被确定为中等风险，则所述处理器适于通过对来自所述至少一个病人的结构化的计算机病历中的、测量动脉硬化负担的信息进行数据挖掘，对所述风险估计进行分级。
26.如权利要求22所述的系统，还包括病人管理模块，所述模块根据所计算的风险估计向所述至少一个病人推荐生活方式改变。
27.如权利要求26所述的系统，其中所述病人管理模块跟踪所述至少一个病人一段时期，并且重新计算所述至少一个病人的风险估计。
28.如权利要求22所述的系统，还包括病人管理模块，所述模块用于根据所计算的风险估计向所述至少一个病人推荐治疗。
29.如权利要求28所述的系统，其中所述病人管理模块跟踪所述至少一个病人一段时期，并且重新计算所述至少一个病人的风险估计。
30.如权利要求22所述的系统，还包括敏感度模块，所述模块用于分析所述至少一个病人的冠状风险因素，以便确定哪个因素将对于影响风险估计最有效果。
31.如权利要求30所述的系统，还包括建模模块，所述模块用于生成冠心病病人的模型，以便模拟不同变量的效果，所述模块被所述敏感度模块使用。
32.如权利要求22所述的系统，还包括成像模块，所述模块用于从图象提取病人信息，并且将所述病人信息存储在结构化的计算机病历中。
33.如权利要求22所述的系统，其中所述处理器根据所计算的估计来确定后继估计的时间。
34.如权利要求22所述的系统，其中所述处理器确定是否所述多个数据域中的至少一个数据域没有被填充，并且推荐要被实施的测试以便填充所述至少一个数据域。
35.如权利要求30所述的系统，其中所述处理器根据所确定的风险因素来确定后继估计的时间。
36.如权利要求30所述的系统，其中所述处理器确定是否所述多个数据域中的至少一个数据域没有被填充，并且如果所述至少一个数据域是所确定的风险因素，则推荐要被实施的测试。
37.如权利要求22所述的系统，其中所述多个结构化的病历中的至少一个包括从挖掘非结构化的数据获得的信息。
38.如权利要求22所述的系统，其中所述多个结构化的病历包括概率信息。
39.如权利要求22所述的系统，其中并非计算风险估计所需要的所有病人信息都包括在所述多个结构化的病历中。
40.如权利要求39所述的系统，其中用概率方法挖掘丢失的信息，并且调整风险估计以在风险估计中包括概率的病人信息。
41.如权利要求40所述的系统，其中所述风险估计是在周期性的间隔中从所述病历自动计算的。
42.如权利要求41所述的系统，其中如果病人的风险估计中存在显著的变化，则所述处理器发出通知。
43.如权利要求42所述的系统，其中如果已经过去足够的时间，则所述处理器发出通知，从而使得病人的风险估计已经显著改变成为可能。
44.一种机器可读的程序存储设备，所述程序存储设备确实包含了机器可执行的指令程序，以便实现用于筛选冠心病的方法步骤，所述方法步骤包括：
检索用于估计冠心病风险的测试，所述测试包括多个关于冠状风险因素的数据域；
访问数据库以便用个别病人的病人信息来填充所述测试的数据域，所述数据库包括计算机病历；以及
计算个别病人患上冠心病的风险估计。
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