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Distributed analyses of disease risk and association across networks of de-identified medical systems.

机译：跨去身份化医疗系统网络的疾病风险和关联的分布式分析。

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摘要

Health information networks continue to expand under the Affordable Care Act yet little research has been done to query and analyze multiple patient populations in parallel. Differences between hospitals relating to patient demographics, treatment approaches, disease prevalences, and medical coding practices all pose significant challenges for multi-site analysis and interpretation. Furthermore, numerous methodological issues arise when attempting to analyze disease association in heterogeneous health care settings. These issues will only continue to increase as greater numbers of hospitals are linked.;To address these challenges, I developed the Shared Health Research Informatics Network (SHRINE), a distributed query and analysis system used by more than 60 health institutions for a wide range of disease studies. SHRINE was used to conduct one of the largest comorbidity studies in Autism Spectrum Disorders. SHRINE has enabled population scale studies in diabetes, rheumatology, public health, and pathology. Using Natural Language Processing, we de-identify physician notes and query pathology reports to locate human tissues for high-throughput biological validation. Samples and evidence obtained using these methods supported novel discoveries in human metabolism and paripartum cardiomyopathy, respectively.;Each hospital in the SHRINE network hosts a local peer database that cannot be overridden by any federal agency. SHRINE can search both coded clinical concepts and de-identified physician notes to obtain very large cohort sizes for analysis. SHRINE intelligently clusters phenotypic concepts to minimize differences in health care settings.;I then analyzed a statewide sample of all Massachusetts acute care hospitals and found diagnoses codes useful for predicting Acute Myocardial Infarction (AMI). The AMI association methods selected 96 clinical concepts. Manual review of PubMed citations supported the automated associations. AMI associations were most often discovered in the circulatory system and were most strongly linked to background diabetic retinopathy, diabetes with renal manifestations, and hypertension with complications. AMI risks were strongly associated with chronic kidney failure, liver diseases, chronic airway obstruction, hemodialysis procedures, and medical device complications. Learning the AMI associated risk factors improved disease predictions for patients in Massachusetts acute care hospitals.
机译：根据《平价医疗法案》，健康信息网络继续扩大，但是很少有研究并行地查询和分析多个患者人群。医院之间在患者人口统计学，治疗方法，疾病患病率和医疗编码规范方面的差异都对多站点分析和解释提出了重大挑战。此外，当尝试分析异构医疗机构中的疾病关联时，会出现许多方法学问题。随着越来越多的医院相连，这些问题将继续增加。；为了解决这些挑战，我开发了共享健康研究信息网络（SHRINE），这是一种分布式查询和分析系统，已被60多家卫生机构广泛使用。疾病研究。 SHRINE被用于进行自闭症谱系障碍中最大的合并症研究之一。 SHRINE已使糖尿病，风湿病，公共卫生和病理学领域的人群规模研究成为可能。使用自然语言处理，我们可以取消对医生注释的识别，并查询病理报告以定位人体组织，以进行高通量生物学验证。使用这些方法获得的样本和证据分别支持人类代谢和围产期心肌病的新发现。； SHRINE网络中的每家医院都拥有一个本地对等数据库，任何联邦机构都无法覆盖该数据库。 SHRINE可以搜索编码的临床概念和身份不明的医生笔记，以获得非常大的队列规模进行分析。 SHRINE智能地对表型概念进行聚类，以最大程度地减少医疗保健设置之间的差异。然后，我分析了马萨诸塞州所有急诊医院的全州样本，发现了可用于预测急性心肌梗死（AMI）的诊断代码。 AMI关联方法选择了96种临床概念。对PubMed引用的手动审核支持自动关联。 AMI关联最常在循环系统中发现，并且与本底糖尿病性视网膜病变，具有肾脏表现的糖尿病和合并症的高血压密切相关。 AMI的风险与慢性肾功能衰竭，肝病，慢性气道阻塞，血液透析程序和医疗器械并发症密切相关。了解AMI相关的危险因素可以改善马萨诸塞州急诊医院患者的疾病预测。

著录项

  • 作者

    McMurry, Andrew J.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Bioinformatics.;Biomedical engineering.;Health sciences.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 159 p.
  • 总页数 159
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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