An Information Fusion Approach to Longitudinal Health Records
纵向健康记录的信息融合方法
基本信息
- 批准号:8722624
- 负责人:
- 金额:$ 30.05万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2012
- 资助国家:美国
- 起止时间:2012-09-01 至 2016-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressCharacteristicsClinicClinicalClinical Decision Support SystemsClinical ResearchClinical TrialsClinical trial protocol documentComplexConsultationsDataData SourcesElectronic Health RecordElectronicsEligibility DeterminationEnrollmentEventFailureGoalsHourLaboratoriesManualsMeasuresMethodsNatural Language ProcessingNaturePatient RecruitmentsPatientsProcessRandomizedRandomized Controlled TrialsRecording of previous eventsRecruitment ActivityResearchSemanticsSensitivity and SpecificitySeriesSourceSpeedStructureTechniquesTestingTimeTimeLineWorkbasecohortcostdesignfallshealth recordimprovedscreening
项目摘要
DESCRIPTION (provided by applicant): Our goal is to leverage an information fusion approach to integrate structured and unstructured information to generate a longitudinal health record (LHR) for accelerating the pace at which patients can be recruited into clinical trials. Because electronic health records (EHR) contain clinical summaries of a patient's clinical history, one would assume that they could be easily leveraged to automatically screen and identify potentially eligible patients. However most EHRs are not well designed to support screening of eligible patients and are composed of multiple data sources that are often redundant or inconsistent, stored in uncoordinated unstructured clinical narratives and structured data. These characteristics make EHRs difficult to use for matching patients against the complex event and temporal criteria of clinical trials protocols. This research proposes that an improved LHR, which contains a comprehensive clinical summary of a patient, can improve patient screening. We propose using a method of information fusion to generate this LHR, which merges information from multiple data sources, that addresses both the meaning and temporal nature of data, such that the resulting information is more accurate than would be possible if these sources were used individually.
The specific aims are to: 1) characterize the barriers of using EHR sources for screening in terms of data redundancy, inconsistency, lack of structure, and temporal imprecision; 2) automatically extract information from unstructured EHR sources necessary for screening patients against clinical trials eligibility criteria using natural language processing; 3) developan LHR appropriate for screening patients against eligibility criteria using information fusion methods based on semantic and temporal information; and 4) evaluate the accuracy of an LHR formed through information fusion for screening patients against clinical trials eligibility critera.
The respective hypotheses to be tested are: 1) Different parts of the EHR will contain variable amounts of redundancy, inconsistency, and temporal imprecision. Some sources will be more valuable for matching patients than others to clinical trials eligibility criteria. 2) Including th information contained in the unstructured notes will reduce the false positive rate of identifying potentially eligible patients over leveraging only the structured data in the EHR. 3) By using information fusion methods based on leveraging semantic and temporal information on a combination of structured and unstructured data, we will be able to accurately summarize the information contained in uncoordinated EHR data sources into an LHR that can be used for screening patients for clinical trials. 4) The use of information fusion to generate a longitudinal
health record will increase the sensitivity and specificity of electronic clinical trial screening ver using a traditional EHR.
With an LHR formed through information fusion for screening patients for clinical trials eligibilit, we will be able to not only reduce the amount of staff effort required to recruit a patient into a clinical trial, but also accelerate the pace at which clinical trials can be conducted.
描述(由申请人提供):我们的目标是利用信息融合方法来整合结构化和非结构化信息,以生成纵向健康记录(LHR),从而加快患者招募到临床试验中的速度。由于电子健康记录(EHR)包含患者临床病史的临床摘要,因此可以假设它们可以很容易地用于自动筛选和识别潜在的合格患者。然而,大多数EHR的设计不适合支持筛选合格患者,并且由多个数据源组成,这些数据源通常是冗余或不一致的,存储在不协调的非结构化临床叙述和结构化数据中。这些特征使得EHR难以用于将患者与临床试验方案的复杂事件和时间标准进行匹配。这项研究提出,一个改进的LHR,其中包含一个全面的临床总结的病人,可以改善病人的筛选。我们建议使用一种信息融合的方法来生成这种LHR,它合并了来自多个数据源的信息,解决了数据的意义和时间性质,这样得到的信息比单独使用这些数据源时更准确。
具体目标是:1)在数据冗余、不一致、缺乏结构和时间不精确方面表征使用EHR源进行筛选的障碍; 2)使用自然语言处理从非结构化EHR源自动提取针对临床试验合格标准筛选患者所需的信息; 3)基于语义和时间信息的信息融合方法,开发出适合于筛选患者的LHR;以及4)评估通过信息融合形成的LHR用于针对临床试验合格性标准筛选患者的准确性。
要测试的相应假设是:1)EHR的不同部分将包含可变数量的冗余,不一致和时间不精确。有些来源对于匹配患者比其他来源更有价值,以符合临床试验的资格标准。2)与仅利用EHR中的结构化数据相比,包括非结构化笔记中包含的信息将降低识别潜在合格患者的假阳性率。3)通过使用基于结构化和非结构化数据组合的语义和时间信息的信息融合方法,我们将能够准确地将不协调的EHR数据源中包含的信息汇总到可用于筛选临床试验患者的LHR中。4)利用信息融合生成纵向
与传统的电子病历相比,电子病历将提高电子临床试验筛选的敏感性和特异性。
通过信息融合形成的LHR用于筛选临床试验killbilit的患者,我们不仅能够减少招募患者参加临床试验所需的工作量,而且还可以加快临床试验的进行速度。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
ALBERT M LAI其他文献
ALBERT M LAI的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('ALBERT M LAI', 18)}}的其他基金
METEOR-Data Synthesis and Transfer (METEOR-DST)
METEOR-数据合成和传输 (METEOR-DST)
- 批准号:
10715025 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
An Information Fusion Approach to Longitudinal Health Records
纵向健康记录的信息融合方法
- 批准号:
8373437 - 财政年份:2012
- 资助金额:
$ 30.05万 - 项目类别:
相似海外基金
CAREER: First-principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics
职业:复杂浓缩合金中化学顺序的第一原理预测性理解:结构、动力学和缺陷特征
- 批准号:
2415119 - 财政年份:2024
- 资助金额:
$ 30.05万 - 项目类别:
Continuing Grant
The effects of human lower limb immobilization on regenerative skeletal muscle stem cell characteristics in vitro
人体下肢固定对体外再生骨骼肌干细胞特性的影响
- 批准号:
MR/Y033787/1 - 财政年份:2024
- 资助金额:
$ 30.05万 - 项目类别:
Research Grant
Socio-Emotional Characteristics in Early Childhood and Offending Behaviour in Adolescence
幼儿期的社会情感特征和青春期的犯罪行为
- 批准号:
ES/Z502601/1 - 财政年份:2024
- 资助金额:
$ 30.05万 - 项目类别:
Fellowship
An Atomic Level Understanding of Optimal Characteristics of TiO2 Protection Layers and Photoelectrode/TiO2 Interfaces for Efficient and Stable Solar Fuel Production
从原子水平了解 TiO2 保护层和光电极/TiO2 界面的最佳特性,以实现高效、稳定的太阳能燃料生产
- 批准号:
2350199 - 财政年份:2024
- 资助金额:
$ 30.05万 - 项目类别:
Continuing Grant
Research on Physical Agents that Reduce Isolation and Loneliness According to the Individual Characteristics of the Elderly
根据老年人个体特征减少孤立感和孤独感的物理疗法研究
- 批准号:
23H00484 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (A)
Assessment of the impact of catchment geology and groundwater discharge characteristics on river water quality and red tide formation in estuarine areas
流域地质和地下水排放特征对河口地区河流水质和赤潮形成的影响评估
- 批准号:
23H00724 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (B)
Functional evaluation of spatio-temporal characteristics of electrical retinal stimulation by temporal interference
时间干扰视网膜电刺激时空特征的功能评估
- 批准号:
23K09025 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Advancement in prediction of hydrodynamic characteristics under the free-surface and discharge evaluation by using the turbulence information measured by live camera images
利用实时相机图像测量的湍流信息预测自由表面下的水动力特性和流量评估的进展
- 批准号:
23K04043 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Vernacular Stone Masonry Houses of Bhutan: A study on the Architectural Characteristics and the Suitable Approach for Protection as Cultural Heritage
不丹乡土石砌房屋:建筑特征和文化遗产保护的适当方法研究
- 批准号:
23H01596 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (B)
For coexistence with Echinococcus - Re-evaluation of biological characteristics of Hokkaido-prevalent population based on the new findings
与棘球蚴共存 - 根据新发现重新评估北海道流行人群的生物学特性
- 批准号:
23H02369 - 财政年份:2023
- 资助金额:
$ 30.05万 - 项目类别:
Grant-in-Aid for Scientific Research (B)