Scaling Biosense: Advanced Informatics Solution

Biosense正在快速整合新的数据源和数据类型。随着Biosense的发展，检测的灵敏度和特异性将取决于如何解决数据集成问题，包括数据延迟、来自某些区域的稀疏数据和异质输入信号。如果没有自动化的方法来解决这些基本问题，Biosense将很难扩大规模。我们将开发一个符合PHIN标准的系统化方法管道，以最大限度地提高灵敏度和特异性的方式自动评估和整合新信号到Biosense中。管道的三个主要阶段是：1）评估和调整数据可用性- Biosense数据采集持续受到 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的敏感性。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缺失数据来提高检测的灵敏度和特异性。我们还将使用多变量方法来帮助区分数据可用性的变化和实际事件计数的变化。2)确定最佳汇总方法-数据汇总方法直接影响检测的灵敏度和特异性。我们将通过系统地确定数据建模的最佳聚集水平来提高检测的灵敏度和特异性。我们还将使用无监督聚类方法以最大限度地提高灵敏度和特异性的方式对数据进行分组。3)集成多个信号-随着Biosense的发展，包括更多的数据源和分析方法，需要跟踪的信号数量将迅速增长到一个水平，以满足Biosense生物智能系统的需求。我们将通过优化整合多种信号来提高灵敏度和特异性 使用非参数多变量建模方法。我们还将开发经验优化的 多变量阈值函数来整合多个单变量检验统计量。开发的PHIN兼容方法将被发布为开源，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，并优化数据聚合数据并将其集成到现有的Bisoense系统中。

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 delays that 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.