Validation of a Remote Wireless Sensor Network (WSN) Approach to the Individualiz

描述(由申请人提供)：可卡因成瘾是一种高度复杂的临床表型，其中可卡因使用/中毒的药理效应(例如，生理、行为和主观)在很大程度上是可预测的和高度特征的，植根于一个独特的身体、心理和社会个体。后一种现实构成了临床药物遗传学方面的挑战，涉及解析成瘾个体和他们的治疗反应的异质性。因此，更现代的临床表型和治疗监测方法需要超越对药物使用的静态、横断面、回溯性评估，并以细粒度和实时的方式更全面、准确和动态地评估成瘾个体。着眼于开发这种方法，目前的申请汇集了两个成熟的研究团队，他们在人类可卡因自我管理和药物开发(耶鲁大学马利森)和移动和普及无线传感、传感器数据分析/推理以及嵌入式和网络系统技术(Ganesan，UMassAmherst)方面拥有互补的跨学科专业知识。他们建议共同开发“特征”(即，基于动态贝叶斯网络分析得出的低水平生理传感器数据的推断算法)，这些特征对检测可卡因的使用/中毒既敏感又特异。提出了两个具体目标，包括：具体目标1：住院患者人体实验室验证、门诊真实世界改进和探索性临床试验试行远程无线传感器网络方法以检测人类可卡因成瘾者的可卡因使用/中毒(N=24)；具体目标2：设计在现实世界环境中可靠地检测可卡因的算法、用于理解可卡因使用与其他环境之间关系的推理技术、以及一个可供其他研究人员轻松复制和用于实地研究的系统(即，一个工具包，其中将包括用于研究设计、实时数据质量分析、推理工具、教程以及云存储和计算资源的Secur基于网络的配置工具)。我们认为，这样的系统具有巨大的长期潜力，可以实现内在地嵌入临床药物遗传学挑战的机会，即为吸食可卡因成瘾的个人开发更高度个性化和更有效的治疗干预措施。

DESCRIPTION (provided by applicant): Cocaine addiction is a highly complex clinical phenotype, one in which largely predictable and highly characteristic pharmacological effects of cocaine use/intoxication (e.g., physiological, behavioral, and subjective) take root in a unique physical, psychological and social person. The latter realities pose a clinical pharmacogenetic challenge with respect to parsing heterogeneity among addicted individuals and their treatment response. Thus, more modern methods of clinical phenotyping and treatment monitoring are required methods that go beyond static, cross- sectional, retrospective assessments of drug use and more holistically, accurately, and dynamically assess the addicted individual in a fine-grained and real-time fashion. With an eye towards developing such methods, the current application brings together two established research teams with complimentary interdisciplinary expertise in human cocaine self-administration and medications development (Malison, Yale) and mobile and pervasive wireless sensing, sensor data analysis/inference, and embedded and networked system technologies (Ganesan, UMass Amherst). Together, they propose to develop "signatures" (i.e., inference algorithms based on low-level physiological sensor data as derived by Dynamic Bayesian Network analysis) that are both sensitive and specific for detecting cocaine use/intoxication. Two specific aims are proposed, and include: Specific Aim 1: Inpatient human laboratory validation, outpatient real world refinement and exploratory clinical trial piloting of a remote wireless sensor network approach to detecting cocaine use/intoxication in human cocaine addicts (N=24); and Specific Aim 2: Designing an algorithm for reliably detecting cocaine in real-world settings, inference techniques for understanding the relationship between cocaine use and other contexts, and a system (deliverable) that can be easily replicated and used by other researchers for field studies (i.e., a toolkit that will include secur web-based configuration tools for study design, real-time data quality analysis, inference tools, tutorials, and cloud storage and computation resources). We believe that such a system has enormous long-term potential for realizing the opportunity instrinsically embedded within the clinical pharmacogenetic challenge, namely, the development of more highly personalized, and in turn, more effective, treatment interventions for individuals addicted to cocaine.