Probabilistic Disease Surveillance

概率疾病监测

• 批准号: 8708209
• 负责人: MICHAEL MATTHEW WAGNER
• 金额: $ 53.58万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8708209
• 关键词: Accident and Emergency department; Advanced Development; Area; Bayesian Method; Characteristics; Clinical; Code; Communicable Diseases; Complex; Computer Systems; Computerized Medical Record; County; Data; Data Sources; Decision Making; Detection; Diagnosis; Diagnostic; Disease; Disease Outbreaks; Disease model; Epidemic; Epidemiologist; Epidemiology; Evaluation; Future; Health; Healthcare; Healthcare Systems; Incidence; Individual; Influenza; Intervention; Knowledge; Laboratories; Lung diseases; Measures; Methods; Modeling; Monitor; Morbidity - disease rate; Natural Language Processing; Patients; Performance; Physicians; Population; Probability; Public Health; Public Health Practice; Publications; ROC Curve; Reporting; Research; Schools; Sensitivity and Specificity; Severities; Simulate; Sodium Chloride; Structure; Support System; System; Systems Integration; Testing; Text; Time; Topaz; Universities; Utah; Vaccination; advanced system; base; computer code; diagnostic accuracy; disorder control; follow-up; improved; influenza outbreak; innovation; interest; knowledge base; mortality; novel; novel strategies; operation; pandemic disease; population health; portability; reproductive; respiratory; surveillance data

DESCRIPTION (provided by applicant): The proposed research will further develop and evaluate a probabilistic approach to disease surveillance. In this approach, a probabilistic case detection system (CDS) uses Bayesian diagnostic networks to compute the likelihoods of patient findings for each of a set of infectious diseases for every patient in a monitored population. CDS computes these likelihoods from data in electronic medical records, including information derived from free-text reports by natural language processing. CDS makes those estimates available to a probabilistic outbreak detection and characterization component (ODCS). ODCS also utilizes a Bayesian approach to compute the probability that an outbreak is ongoing for each of a set of infectious diseases of interest, given information from CDS. ODCS also computes probability distributions over the current and future size of a detected outbreak and other characteristics such as incubation period used by public health officials when responding to an outbreak. The proposed research will extend the approach, which we have already developed and evaluated for the disease influenza to six additional respiratory infectious diseases. The research will also extend the capabilities of ODCS to utilize non-EMR data, detect an unknown disease, and detect and characterize concurrent outbreaks. The planned evaluations will measure the accuracy of both CDS and ODCS using historical surveillance data from two regions and simulated outbreak data, which we will create by adding outbreak cases generated by an agent-based epidemic simulator to real baseline surveillance data from non-outbreak periods. The innovation being advanced by this research is a novel, integrated, Bayesian approach for the early and accurate detection of cases of diseases that threaten health and for the detection and characterization of outbreaks of diseases that threaten public health. The proposed approach has significant potential to improve the information available to public health officials and physicians, which can be expected to improve clinical and public health decision making, and ultimately to improve population health.

描述(由申请人提供)： 拟议的研究将进一步发展和评估疾病监测的概率方法。在这种方法中，概率病例检测系统(CDS)使用贝叶斯诊断网络来计算被监测人群中每个患者的一组传染病中每一种患者发现的可能性。CDS根据电子医疗记录中的数据计算这些可能性，包括通过自然语言处理从自由文本报告中获得的信息。CDS将这些估计提供给概率暴发检测和特征部分(ODCS)。 疾病预防控制和预防中心还利用贝叶斯方法，根据疾控中心提供的信息，计算一组感兴趣的传染病中的每一种疾病正在暴发的概率。疾病控制和预防中心还计算检测到的疫情当前和未来规模的概率分布，以及公共卫生官员在应对疫情时使用的潜伏期等其他特征。 这项拟议的研究将把我们已经为流感疾病开发和评估的方法扩展到另外六种呼吸道传染病。这项研究还将扩展ODCS的能力，以利用非EMR数据，检测未知疾病，并检测和表征同时爆发的疫情。计划中的评估将使用两个地区的历史监测数据和模拟的疫情数据来衡量CDS和ODCS的准确性，我们将通过将基于代理的流行病模拟器生成的疫情病例添加到来自非暴发期的真实基线监测数据来创建这些数据。 这项研究正在推进的创新是一种新的、综合的贝叶斯方法，用于早期和准确地检测威胁健康的疾病病例，并检测和描述威胁公共健康的疾病的暴发。拟议的方法具有巨大的潜力，可以改善公共卫生官员和医生可获得的信息，这有望改善临床和公共卫生决策，并最终改善人口健康。