CN1613069A - 利用基于群体的分析挖掘病人数据 - Google Patents

利用基于群体的分析挖掘病人数据 Download PDF

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CN1613069A
CN1613069A CNA028266919A CN02826691A CN1613069A CN 1613069 A CN1613069 A CN 1613069A CN A028266919 A CNA028266919 A CN A028266919A CN 02826691 A CN02826691 A CN 02826691A CN 1613069 A CN1613069 A CN 1613069A
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R·B·劳
S·桑迪亚
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Siemens Corporate Research Inc
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Abstract

提供了一种用于分析基于群体的病人信息的系统和方法。该方法包括以下步骤：利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；把挖掘出的数据编制成多条结构化的病人记录；输入与感兴趣疾病有关的至少一个病人准则；以及提取与至少一个病人准则相匹配的至少一个结构化的病人记录。该系统包括：数据挖掘器，用于利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘，以及用于把挖掘出的数据编制成多条结构化的病人记录；接口，用于输入与感兴趣疾病有关的至少一个病人准则；和处理器，适用于提取与至少一个病人准则相匹配的至少一个结构化的病人记录。

Description

利用基于群体的分析挖掘病人数据
相关申请的交叉引用
本申请要求了美国临时申请序列号60/335,542的优先权，该申请于2001年11月2日提交，将该篇申请的全部内容引用于此，以供参考。
技术领域
本发明涉及医疗信息处理系统，更具体而言，涉及一种用于根据基于群体的信息来对病人执行结果分析以及用于对基于群体的信息实施追溯性研究的计算机化的系统和方法，其中所述基于群体的信息来源于各种源。
背景技术
正确保健医疗的病人对于他们医疗病症的最佳治疗而言是必不可少的。典型地，根据建立的治疗方针来为患有特殊病症/疾病的病人开药或规定治疗方法。治疗方针尤其概括了具体的药品剂量、应该施用剂量的频率、关于应该如何施用剂量的指示以及用于疗效性治疗的时间线。时常，对于新病人的治疗是直接根据具有少量变化的治疗方针来进行管理的。典型地，这些方针是通过预期的医疗研究而导出的。研究预期的医疗研究，即随机化临床试验，其中研究近在乎理想的条件下通过筛选病人群体来经验主义地测试假设，确保了病人保健竖持不懈地遵循所述方针并且记录下了所有的有关数据。这种作法未能充分利用历史医疗数据，而是仅仅依赖于病人严格遵守治疗方针的成功率。另外，临床试验实施起来也非常昂贵。
历史医疗数据代表分析病人保健过程中有价值的源以及医疗结果。正如所指出的那样，治疗方针仅仅是依据对严格遵守治疗方针的病人的治疗结果而产生的。然而，对病人保健结果有影响的、来自于病人和专业医疗护理的变量数目非常地高。此外，这些变量之间的关系实际上也是未知的。因此，充分地从过去的医疗数据中学习的能力能够大大地提高病人卫生保健。
追溯性研究例如来自于医院的历史医疗病人记录的分析，是对预期的临床试验的补充。卫生保健组织积累着非常大量的病人数据，这些病人数据都是用于知识管理的重要工具。分析这种已收集的信息可能会产生这样的见解，这些见解可能会在将来的试验中得到证实。最重要的是，追溯性研究能够以最少两种方式测量方针在现实临床设置中的影响。首先，追溯性研究能够确定对于病人群体的治疗疗效，这一病人群体是被排除在临床试验之外的。例如，65岁以上的病人或者那些有其它疾病的病人可能会被排除在临床试验之外，然而在那个试验中核实的方针现在却用于治疗所有的医院病人。第二，医院中的病人治疗可能不同于试验中的病人治疗。例如，结肠癌方针要求在外科手术的6个星期内开始化学疗法，这在临床试验中被严格地实施。然而，在医院中，一些病人可以直至外科手术的10个星期后才开始化学疗法(例如，他们可能病得过于严重或者错过了预约)。这种拖延对病人的结果的影响只能通过追溯性分析来加以确定，这是由于实施会测试这种拖延影响的临床试验是不合乎伦理的，——实际上，阻止了保健的认可标准。
然而，由于多种原因分析医院数据是困难的。首先，医疗数据由于其丰富结构而分析起来非常复杂。许多传统的统计方法不合适于具有结构的数据、时间顺序的事件数据(医疗数据具有重要的时间成分)和/或诸如自由文本、图像等等这类不具有结构的数据。第二，因为医院病人数据曾被收集来治疗病人(与收集医院病人数括以供临床试验中分析相反)，所以它在许多方面是不理想的，比如：遗漏的/错误的/不一致的数据；没有记录的关键结果/变量；在数据收集中的偏差，例如，得病的病人比身体好的人获得更多的测试，(这从医疗观察点来看是非常自然的，但是具有内在的假设，即可能会因为许多算法而带来问题)；以及随时间的所收集变量/治疗方法的变化，这尤其会影响到一些长期疾病，这些疾病的治疗可能会跨过十年。最后，在实践中，在确定病人是否按照方针以及是否被正确治疗难以知晓的医疗专业人员当中还存在广泛的变数。
考虑到上述问题，存在对这样一种技术的需要，即从各种源那里收集基于群体的病人信息，以便对收集到的信息执行结果分析，并且以快速的方式对从各种源导出的大量医疗信息实施追溯性分析。
发明内容
提供了一种用于分析基于群体的病人信息的系统和方法。
根据本发明的一个方面，提供了一种用于分析病人记录的方法，该方法包括以下步骤：利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；把挖掘出的数据编制成多条结构化的病人记录；输入与感兴趣疾病有关的至少一个病人准则；以及提取与至少一个病人准则相匹配的至少一个结构化的病人记录。
根据本发明的另一个方面，一种用于分析多条病人记录的系统，该系统包括：数据挖掘器，用于利用与感兴趣疾病有关的领域知识库来从多条病人记录挖掘信息，以及用于把挖掘出的数据编制成多条结构化的病人记录；接口，用于输入与感兴趣疾病有关的至少一个病人准则；和处理器，适用于提取与至少一个病人准则相匹配的至少一个结构化的病人记录。
在本发明的另一个方面，提供了一种用于对多条病人记录实施追溯性分析研究的方法。该方法包括以下步骤：利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；把挖掘出的数据编制成多条结构化的病人记录；输入多个病人准则，这些病人准则形成了与感兴趣疾病有关的假设；以及提取与多个病人准则相匹配的多条结构化的病人记录。该方法还包括根据多条结构化的病人记录来确定病人结果的步骤，以及通过将病人结果与建议的结果进行比较来证实该假设的步骤。
在本发明的另一个方面，一种机器可读的程序存储设备，确实地包括一个指令程序，该指令程序可在机器上执行以实施用于分析病人记录的方法步骤，所述方法步骤包括：利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；把挖掘出的数据编制成多条结构化的病人记录；输入与感兴趣疾病有关的至少一个病人准则；以及提取与至少一个病人准则相匹配的至少一个结构化的病人记录。
附图说明
本发明的上述及其它方面、特征和优势当结合附图，从下面结合附图描述的详细说明中将变得更加明显，在附图中：
图1是一个计算机处理系统框图，根据本发明的一个实施例可以将本发明应用于它；
图2举例说明了示例性的计算机化的病人记录(CPR)；和
图3举例说明了用于挖掘高质量结构化的医疗信息的示侧性数据挖掘框架；
图4举例说明了一个根据本发明实施例的示例性分析系统的框图；和
图5举例说明了根据本发明的实施例、用于分析大量医疗信息的流程图。
具体实施方式
为了辅助对本发明的清晰理解，这里提供了一些说明性示例，它们描述了本发明的确定方面。但是，应该理解这些说明并不是为了限制本发明的范围，而是为了描述与本发明相关的确定概念。
提供了一种用于分析基于群体的医疗数据的系统和方法。根据本发明的实施例，基于计算机的系统把来自于各种源的基于群体的病人数据-例如，结构化的和非结构化的数据编程到结构化的数据库中以供分析。首先，该系统将从计算机化的病人记录(CPR)内的结构化的源(例如，财务的)和非结构化的源(例如，图像的)那里汲取信息。这些数据能够以有意义的方式被自动地提取、组合和分析。
与医务人员查阅每条记录并且调换其发现的传统方法相反，本发明考虑到了以快速方式来分析大量信息。由于信息是从各种源那里收集的，这些源包含与特定病人有关的不同信息，可以分析各种准则(criteria)或变量来确定它们对所建议的治疗或方针的影响。
应该理解本发明可以用硬件、软件、固件、专用处理器或它们的组合的各种形式的来实现。优选地，本发明采用作为确实包括在程序存储设备上的程序的软件来加以实现。程序可被上载到包含任意合适的体系结构的机器上并由其执行。优选地，该机器被实现在拥有下列硬件的计算机平台上：所述硬件例如是一个或多个中央处理单元(CPU)、一个随机访问存储器(RAM)和输入/输出(I/O)接口。该计算机平台还包括操作系统和微指令代码。这里所描述的多个过程和功能可以是通过操作系统执行的微指令代码的一部分或者程序的一部分(或者是它们的组合)。另外，多种其它外围设备可以被连接到该计算机平台，诸如附加数据存储设备和打印设备。
应该理解，因为附图中所描绘的组成系统部件和方法步骤中的一些优选地是以软件实现的，所以系统部件(或过租步骤)之间的实际连接可能有所不同，取决于编程本发明的方式。
图1是计算机处理系统100的框图，在本发明的一个实施例中，可以将本发明应用于它。系统100包括至少一个处理器(下文称处理器)102，它通过系统总线104和其它部件可操作地相耦合。只读存储器(ROM)106、随机访问存储器(RAM)108、I/O接口110、网络接口112和外部存储器114都与系统总线104可操作地相耦合。各种外国设备，例如显示设备、盘式存储设备(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与系统总线104可操作地相耦合。
计算机系统100可以是独立系统或通过网络接口112与网络相连。网络接口112可以是硬布线接口。然而，在多种示例性实施例中，网络接口112可以包括适于向另一设备传送信息或从另一设备传出信息的任意设备，诸如通用异步收发器(UART)、并行数字接口、软件接口或已知或后来开发的软件和硬件的任意组合。网络接口可以连接到不同类型的网络，包括局域网(LAN)、广域网(WAN)、内联网、虚拟专用网(VPN)和因特网。
外部存储器114可以用数据库管理系统(DBMS)来实现，其由处理器102加以管理并驻留在像硬盘这样的存储器上。但是，应该意识到外部存储器114可以实现在一个或多个附加计算机系统上。例如，外部存储器114可以包括驻留在一个单独的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偏离本发明的精神和范围的前提下也可以使用其它替代的计算环境。
卫生保健提供商越来越多地使用自动化的技术来存储和检索信息。使用计算机化的病人记录(CPR)来维护病人信息就是一个这样的例子。如图2所示，示侧性CPR(200)包括在病人治疗期间收集到的信息。这一信息可以包括，例如，计算机X线断层造影(CT)图像、X-光图像、实验室测试结果、医生进展注释、与医疗过程有关的细节、处方药信息、放疗报告、其它专家报告、人口统计学信息和计帐(财务)信息。
一个CPR通常源于多个数据源，每个数据源通常反映病人保健的不同方面。结构化的数据源，例如财务、实验室和药房数据库，通常用数据库表维护病人信息。信息也可存储在非结构化的数据源中，例如自由文本、图像和波形。通常，关键的临床发现都只存储在医师报告中，例如口述。
图3说明了用于利用如下数据挖掘技术来挖掘高质量结构化的临床信息的示例性数据挖掘系统，所述数据挖掘技术在同本申请一起提交的Rao等人的“Patient Data Mining(病人数据挖掘)”的共同待审美国专利申请序列号10/＿＿,＿＿，(代理人证号：8706-600)中作了描述，将其全部内容引入于此以供参考。该数据挖掘系统包括数据挖掘器(350)，它使用知识库(330)中包含的领域专用的知识从CPR(310)挖掘信息。数据挖掘器(350)包括从CPR提取信息的部件(352)、随时间用有原则的方式组合所有可用证据的部件(354)以及从这个组合过程做出推理的部件(356)。挖掘出的信息可以被存储在结构化的CPR(380)中。照此，包含在CPR中的所有信息，不论它们是来自于结构化的源还是非结构化的源，都将以结构化的方式加以存储。
提取部件(352)负责从与病人有关的每个数据源收集小块信息，这些信息被表示为在特定时间的、与病人有关的概率断言。这些概率断言被称为元素。组合部件(354)组合在相同时间周期中引用相同变量的所有元素以形成一个与该变量有关的统一的概率断言。这些统一的概率断言被称为仿真陈述(factoid)。推理部件(356)在相同和/或不同的时间点上处理对这些仿真陈述的组合，以产生病人状态随时间进展的连贯且简洁的图片。病人状态的这种进展被称为状态序列。
本发明能够建立病人状态的单独模型。病人状态仅仅是人们能够关心的、与病人有关的变量或准则的集合。所感兴趣的信息可能包括一个状态序列，即在病人治疗期间在不同时间点上的病人状态的值。
上述组件中的每一个都使用与感兴趣的领域(诸如，例如，感兴趣的疾病)有关的详细知识。这个领域知识库(330)可以有两种形式。它可被编码为系统的输入，或者编码为产生能够被系统所理解的信息的程序。领域知识库(330)中被输入系统的当前形式的那部分也可从数据获悉。
如上所述，提取部件(352)从CPR(310)获取信息以产生与病人有关的概率断言(元素)，这些断言是时刻或与时段有关。这个过程在领域知识库(330)中所包含的领域知识的引导下实施。提取所需的领域知识对每个数据源来说通常是特定的。
参照图4，举例说明了一个根据本发明实施例的示例性分析系统400。该分析系统400包括处理器402，其用于从结构化的数据库380中提取信息以及用于对提取出的信息执行不同任务。另外，处理器402适合于通过I/O接口人工地接收所输入的病人准则或变量414，往往使用所述接口来从数据库380中提取特定的信息。由分析系统200执行的每个任务都是通过驻留在系统402的处理器中和/或该系统的存储设备(例如，RAM、ROM、外部存储器等)中的可执行模块来执行的。
参照图5，提供了举例说明分析基于群体的数据的方法的流程图。例如，对于特殊医院，可以检查有糖尿病的病人中不满意结果的问题(例如，临床的、财务的以及停留时间)，所述的有糖尿病的病人持续肌萎缩梗塞(myocardial infarction)。
首先，例如特定医院中，在治疗大量病人的过程期间中，随着时间将多种计算机化的病人记录聚集起来。利用与感兴趣疾病有关的领域知识库来挖掘这一历史数据，并且将其编制到结构化的CPR数据库中(步骤502)。例如，从各种源中提取信息，以便鉴别确诊的急性心肌梗塞(AMI)的病人。这将不会基于ICD-9代码(它具有约90％的准确度)，而是基于符合MONICA准则的临床发现、实验室发现和EKG发现的组合，该MONICA准则是国际上为鉴别AMI病人而采用的标准。
与感兴趣疾病有关的一个或多个准则或变量被输入到系统中(步骤504)。该系统从遵循所述准则的结构化的数据库中提取病人记录(步骤506)。例如，一旦AMI病人被鉴别出来，该系统就将根据药剂学数据和实验室数据分离出有糖尿病(例如，准则)的病人的子集，所述药剂学数据显示出了对施用胰岛素或其它抗糖尿病药剂的需要，所述实验室数据显示出了高血糖。
然后，该系统为提取出的病人记录确定病人结果(步骤508)，并且输出结果。至少一个病人准则值可以被改变，以便确定在准则值上的变化将如何影响所述结果(步骤510)。最后，该系统将编制和输出所述结果，以便适当的工作人员能够进行查阅(步骤512)。该系统鉴别临床结果中的差异，例如死亡、步骤(冠状动脉旁路手术或血管成形术)、传染等等，并且将这些结果置于附带的财务数据、混合情况数据、治疗数据、疗法数据以及停留时间数据的上下文环境当中。所述输出可以是举例说明在相对于病人结果的准则方面变化影响的图表、表格、曲线等等。
在另一个实施侧中，本发明的系统和方法将对特殊病人执行结果分析，例如医生可能想要确定用来降低病人胆固醇含量的最佳处方药物。该系统将提取胆固醇含量超过预定限度例如-250的病人的病人记录。医生将输入与病人目前状态相关的准则或变量414，例如，年龄、血压、LDL胆固醇、HDL胆固醇等等。处理器402于是将与结构化的CPR数据库380进行交互，以便提取选取匹配目前病人的准则的病人记录，并将输出所提取的记录的病人结果对药物治疗。医生可以改变一个或多个准则或变量(例如不同药物的用途)的值，改变为病人烟瘾等等，以便确定结果如何受到改变的影响，其中该系统将提取新的病人记录以便根据新的变量组来反映病人结果。由于该系统能够根据不同的准则来从大量记录中提取不同的病人记录，因此该系统能够比试图用手搜索带相似信息的病人记录的传统方式更快地执行结果分析。
另外，该系统可以用于通过使输入的准则与确定的结果相关连，来产生对于潜在的预期的临床试验的假设。
在另一个实施例中，可以采用本发明的系统和方法来实施追溯性研究。在预期的临床试验期间，可以观察特殊人群，例如年龄25到40岁的男性，来确定用于治疗特殊疾病的最适当的方针。随后，接着在无进一步测试的情况下，将从临床试验中研发出的方针应用于所有年龄组。本发明的系统和方法将通过提取匹配实际试验期间创建的方针的病人记录来允许将要实施在排除于试验之外的人们身上的研究，但是将受到所输入的病人准则的限制，例如，年龄40到50岁的女性。本发明的系统和方法允许在不需要有人人工地查阅大量记录的情况下、对大量人群实施追溯性分析研究。
此外，可以实施追溯性研究来证实通过使输入的准则与确定的病人结果相关连、且然后将确定的病人结果与建议的假设病人结果进行比较来产生假设而加以实施。
本发明的分析系统和方法提供来自于各种源的大量数据(即结构化的和非结构化的数据)的集合，以便以高效且快速的方式加以分析。该方法和系统将通过允许医疗专业人员对基于群体的病人信息执行病人结果分析来提供改善的保健质量，例如医院治疗的大量病人，以便确定最适当的治疗方法。另外，与常规的单独查阅和分析相反，本发明的系统和方法将通过允许将通过从各种源中挖掘数据而自动执行的追溯性研究来为研究更和医院降低成本。
尽管在这里已经参考附图描述了本发明的说明性实施例，应该理解本发明并不受限于那些具体的实施例，在不偏离本发明的范围和精神的前提下本领域的技术人员可以进行多种其它的改动和变更。

Claims (20)

1.一种用于分析病人记录的方法，该方法包括以下步骤：
利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；
把挖掘出的数据编制成多条结构化的病人记录；
输入与感兴趣疾病有关的至少一个病人准则；以及
提取与所述至少一个病人准则相匹配的至少一个结构化的病人记录。
2.如权利要求1所述的方法，还包括确定所述至少一个结构化的病人记录的病人结果的步骤。
3.如权利要求2所述的方法，还包括改变至少一个病人准则的值的步骤，和重复提取步骤和确定步骤。
4.如权利要求1所述的方法，其中多条病人记录都被存储在结构化的和非结构化的源中。
5.如权利要求1所述的方法，其中多条病人记录都是在多个病人的治疗过程中收集的。
6.如权利要求2所述的方法，还包括使多个准则与多个病人结果相关连的步骤。
7.如权利要求6所述的方法，还包括根据相关性来建议用于临床试验的假设的步骤。
8.如权利要求7所述的方法，还包括通过对多条结构化的病人记录执行追溯性研究来证实该假设的步骤。
9.一种用于分析多条病人记录的系统，所述多条病人记录被存储在结构化的和非结构化的源中，该系统包括：
数据挖掘器，用于利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘，以及用于把挖掘出的数据编制成多条结构化的病人记录；
接口，用于输入与感兴趣疾病有关的至少一个病人准则；和
处理器，适用于提取与所述至少一个病人准则相匹配的至少一个结构化的病人记录。
10.如权利要求9所述的系统，还包括用于存储多条结构化的病人记录的数据库。
11.如权利要求9所述的系统，其中处理器进一步适应于确定至少一个结构化的病人记录的病人结果。
12.如权利要求11所述的系统，其中处理器进一步适应于使多条准则与多个病人结果相关连。
13.如权利要求12所述的系统，其中处理器进一步适应于根据相关性来建议用于临床试验的假设。
14.一种用于对多条病人记录实施追溯性研究的方法，该方法包括以下步骤：
利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；
把挖掘出的数据编制成多条结构化的病人记录；
输入多个病人准则，这些病人准则形成了与感兴趣疾病有关的假设；以及
提取与多个病人准则相匹配的多条结构化的病人记录。
15.如权利要求14所述的系统，还包括根据多条结构化的病人记录来确定病人结果的步骤。
16.如权利要求15所述的系统，还包括通过将病人结果与建议的结果进行比较来证实该假设的步骤。
17.一种机器可读的程序存储设备，确实地包括一个指令程序，该指令程序可在该机器上执行，以实施用于分析病人记录的方法步骤，所述方法步骤包括：
利用与感兴趣疾病有关的领域知识库来对多条病人记录进行数据挖掘；
把挖掘出的数据编制成多条结构化的病人记录；
输入与感兴趣疾病有关的至少一个病人准则；以及
提取与至少一个病人准则相匹配的至少一个结构化的病人记录。
18.如权利要求17所述的程序存储设备，还包括确定至少一条结构化的病人记录的病人结果的方法步骤。
19.如权利要求18所述的程序存储设备，还包括改变至少一个病人准则的值的方法步骤，并且重复提取步骤和确定步骤。
20.如权利要求17所述的程序存储设备，其中多条病人记录都被存储在结构化的和非结构化的源中。
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