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Building Complex Disease Models using Ontologies and Data Repositories

使用本体和数据存储库构建复杂的疾病模型

基本信息

批准号：
7836640
负责人：
PETER John HAUG
金额：
$ 75.81万
依托单位：
IHC HEALTH SERVICES, INC.
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-15 至 2013-09-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7836640
关键词：
Address Algorithms Antidotes Area Caregivers Caring Characteristics Clinical Clinical Data Complex Computer Systems Computer software Critiques Data Data Analyses Data Element Databases Development Diagnosis Dictionary Disease Disease Management Disease Outcome Disease model Documentation Electronic Health Record Electronics Goals Guidelines Healthcare Healthcare Systems Hybrids Information Technology Institution Knowledge Learning Link Measures Medical Medicine Methods Modeling Modern Medicine Ontology Outcome Patients Pattern Pattern Recognition Process Publishing Quality of Care Records Research Research Infrastructure Resources SNOMED Clinical Terms Semantics Solutions Source Structure System Technology Terminology Testing Therapeutic Therapeutic Intervention Time Training Unified Medical Language System Work base biomedical ontology care episode clinical care clinical decision-making computerized tools data mining data modeling design electronic data evidence based guidelines human disease novel outcome forecast point of care prototype research study response success tool

项目摘要

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (10) Information Technologies for Processing Healthcare Data and specific Challenge Topic, 10-LM-102: Advanced Decision Support for Complex Clinical Decisions. A key direction for our research has been the development of information technologies that can focus and extend the ability of the clinician to make informed medical decisions at the patient bedside. The aim of the current project is to explore a novel modeling technology which may help organize a patient's clinical information and assist in its interpretation. This technology combines a disease ontology with computational tools borrowed from data mining. The ontology is designed to capture the terminology of medicine and to represent key relationships among medical concepts. In addition, it will contain links to data in an active Electronic Health Record (EHR). Using the ontology's semantic infrastructure as a framework, we will overlay computational tools that will support medical pattern recognition and prediction. This hybrid model should effectively recognize patterns that 1) define and diagnose human disease, 2) identify comorbid and complicating factors, 3) identified disease and patient specific therapeutic interventions, and 4) predict outcomes in the context of relevant patient characteristics. If the model proves sufficiently accurate, it can be embedded in applications that assist with diagnosis, documentation, therapeutic planning, and prognosis at the patient bedside. Our approach will be to develop efficient strategies to combine the data models used in Electronic Health Records (EHRs) with published ontologies and other semantic representations. The goal is an ontology that both represents the relationships described above and effectively links to the data models native to an active EHR. Data extracted from this EHR and collected and maintained in an Enterprise Data Warehouse (EDW) will be used to train the computable component of the hybrid model. The rationale for the development of this hybrid technology is to supplant the labor-intensive and time- consuming process used to develop evidence-based guidelines for use in standardizing clinical care. In these efforts, the availability of medical expertise is the rate limiting feature. We seek to develop an automated method that will substantially replace the need for medical experts in the development of guidelines. We will test the success of this approach by implementing this hybrid model for a group of diseases which have been studied extensively in our healthcare system. In this setting we will develop and test a prototype of this computable clinical ontology. The goal of this project is to bring together two technologies to create a mechanism for generating useful medical knowledge. The technologies involved are special electronic dictionaries (ontologies) that describe the way that medical concepts are related and tools that can be trained with information from previous episodes of care to detect diseases, suggest treatments, and predict disease outcomes. We will conduct tests to determine whether the combination of these technologies can be used by medical computing systems to aid in the management of disease by advising caregivers at the bedside.

描述（由申请人提供）：此应用程序解决了广泛的挑战领域（10）处理医疗保健数据的信息技术和特定的挑战主题，10-LM-102：复杂临床决策的高级决策支持。我们研究的一个关键方向是信息技术的发展，可以集中和扩展临床医生在病人床边做出明智的医疗决策的能力。目前的项目的目的是探索一种新的建模技术，可以帮助组织病人的临床信息，并协助其解释。该技术将疾病本体与从数据挖掘中借来的计算工具相结合。本体的目的是捕捉医学术语，并表示医学概念之间的关键关系。此外，它还将包含指向有效电子健康记录（EHR）中数据的链接。使用本体的语义基础设施作为一个框架，我们将覆盖计算工具，将支持医疗模式识别和预测。该混合模型应有效识别以下模式：1）定义和诊断人类疾病，2）识别共病和并发因素，3）识别疾病和患者特异性治疗干预，以及4）在相关患者特征的背景下预测结果。如果模型被证明足够准确，它可以嵌入到应用程序中，帮助诊断，文档，治疗计划和患者床边的预后。我们的方法将是开发有效的策略，联合收割机结合使用的数据模型，在电子健康记录（EHR）与已发布的本体和其他语义表示。我们的目标是一个本体，既表示上述关系，并有效地链接到一个积极的EHR本地的数据模型。从EHR中提取并在企业数据仓库（EDW）中收集和维护的数据将用于训练混合模型的可计算组件。开发这种混合技术的基本原理是取代用于开发用于标准化临床护理的循证指南的劳动密集型和耗时的过程。在这些努力中，医疗专业知识的可用性是限制费率的特征。我们寻求开发一种自动化的方法，将大大取代在制定指南的医学专家的需要。我们将测试这种方法的成功，通过实施这种混合模式的一组疾病，已在我们的医疗保健系统中广泛研究。在这种情况下，我们将开发和测试这个可计算的临床本体的原型。该项目的目标是将两种技术结合起来，创建一种生成有用医学知识的机制。所涉及的技术是特殊的电子词典（本体论），描述了医学概念的相关方式，以及可以用以前护理的信息进行训练的工具，以检测疾病，建议治疗方法和预测疾病结果。我们将进行测试，以确定这些技术的组合是否可以被医疗计算系统用于通过在床边向护理人员提供建议来帮助疾病管理。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Ontology-based tools to expedite predictive model construction.

基于本体的工具可加快预测模型的构建。

DOI：
发表时间：
2014
期刊：
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
影响因子：
0
作者：
Haug,Peter;Holmen,John;Wu,Xinzi;Mynam,Kumar;Ebert,Matthew;Ferraro,Jeffrey
通讯作者：
Ferraro,Jeffrey

A method for the development of disease-specific reference standards vocabularies from textual biomedical literature resources.