Scaling Biosense: Advanced Informatics Solution for Critical Problems

Scaling Biosense：针对关键问题的高级信息学解决方案

• 批准号: 7428899
• 负责人: Ben Y Reis
• 金额: $ 46.41万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2009-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7428899
• 关键词: Scaling; Biosense; Advanced; Informatics; Solution

Biosense is rapidly incorporating new data sources and data types. As Biosense grows, sensitivity and specificity of detection will depend on how the data integration problems are addressed, including data delays, sparse data from some regions, and heterogeneous input signals. Without automated approaches to these fundamental problems, it will be difficult for Biosense to scale. We will develop a systematic pipeline of PHIN-compliant methods that will automate the process of evaluating and integrating new signals into Biosense in a manner that maximizes sensitivity and specificity. The three main stages of the pipeline are: 1) Assessing and adjusting for data availability - Biosense data acquisition is continually subjected to crippling systemic delaysthat drastically reduce the timeliness and radically undermine the sensitivity of the system. We will increase sensitivity and specificity of detection by evaluating data completeness and compensating for missing data using model-based extrapolation. We will also use a multivariate approach to help distinguish between changes in data availability and changes in actual event counts. 2) Determining optimal aggregation approaches - The approach to data aggregation directly affects sensitivity and specifcity of detection.We will increase sensitivity and specificity of detection by systematically determining the best level of aggregation at which to model the data. We will also use unsupervised clustering approaches to group data in the manner that maximizes sensitivity and specificity. 3) Integrating multiple signals - As Biosense grows to include additional data sources and analytic methods, the number of signals that need to be tracked will quickly grow to a level that overwhelms the Biosense Biointelligence Monitors.We will increase sensitivity and specificity by optimally integrating multiple signals using a nonparametric multivariate modeling approach. We will also develop empirically optimized multivariate threshold functions to integrate multiple univariate test statistics. The PHIN-compliant methods developed will be released into open source for the benefit of the public health community. These tools can be used by Biosense profesisonals to evaluate new and existing data sources, assess and adjust for data delays, and optimallydata aggregate the data and integrate it into the existing Bisoense system.

Biosense 正在快速整合新的数据源和数据类型。随着 Biosense 的发展，灵敏度和 检测的特异性将取决于如何解决数据集成问题，包括数据 延迟、某些区域的稀疏数据以及异构输入信号。如果没有自动化的方法 这些根本性问题，让 Biosense 难以扩大规模。我们将开发系统化的管道 符合 PHIN 标准的方法将自动评估新信号并将其集成到 以最大限度提高灵敏度和特异性的方式进行生物传感。管道的三个主要阶段是： 1) 评估和调整数据可用性 - Biosense 数据采集不断受到 严重的系统性延误，大大降低了及时性并从根本上破坏了 系统。我们将通过评估数据完整性和 使用基于模型的外推法补偿丢失的数据。我们还将使用多元方法 帮助区分数据可用性的变化和实际事件计数的变化。 2) 确定最佳聚合方法 - 数据聚合方法直接影响 检测的灵敏度和特异性。我们将通过系统地提高检测的灵敏度和特异性 确定对数据建模的最佳聚合级别。我们还将使用无监督的 以最大化敏感性和特异性的方式对数据进行聚类的方法。 3) 整合多个信号 - 随着 Biosense 不断发展以包含更多数据源和分析方法， 需要跟踪的信号数量将迅速增长到超出 Biosense 的水平 生物智能监测仪。我们将通过优化整合多个信号来提高灵敏度和特异性 使用非参数多元建模方法。我们还将开发经验优化的 多变量阈值函数集成多个单变量测试统计数据。 开发的符合 PHIN 标准的方法将开源，以造福公众健康 社区。 Biosense 专业人员可以使用这些工具来评估新的和现有的数据源， 评估和调整数据延迟，并以最佳方式聚合数据并将其集成到现有的数据中 Bisoense 系统。