Estimating BrAC/BAC from Transdermal Alcohol: Combining First-Principles Physiological Models with Machine-Learning to Create Software to Optimally Process and Quantitatively Interpret Biosensor Data

摘要 透皮酒精生物传感器提供了一种有希望的方法，可以不引人注意地收集连续的酒精浓度。 长期处于自然主义的环境中。现在可以可靠地测量透皮吸收的设备 酒精浓度(TAC)，酒精通过皮肤扩散的量，但经常被忽视 使这些生物传感器变得有价值的关键问题是，TAC与容易 可解释的个人呼吸和血液酒精浓度(BAC/BAC)的测量， 环境条件和设备。这项研究的目标是开发软件，将TAC数据转换为 BRAC/BAC的估算值(eBrAC/EBAC)。我们将通过1)开发数学模型来实现这一目标 从TAC数据生成定量的eBrAC，2)研究校准这些模型的替代选项，3) 使用各种类型和数量的非常精确的数据来测试模型的适用性，以及4)将模型打包到 综合数据同化软件程序。具体地说，我们将通过以下方式提高模型的保真度 将先进的基于物理/生理的模型与统计方法和数据驱动的机器相结合- 学习技巧。为了减轻目前为每个人校准模型所需的负担，我们将 测试一些校准程序，包括更换实验室的酒精管理 会议与更多不同的饮酒方案以及基于人口的参数估计。我们将测试 我们的模型和协议使用收集的详细消费数据1)两名调查人员，2)40名 参与者，每个人将参加四次受控的实验室饮酒会议，以及3)40名参与者 世卫组织将各自参加实地试验和实验室会议。我们将检查模特在饮酒时的合适性 使用不同量的个性化酒精数据(例如，呼吸分析仪、饮酒日记)时的模式 校准模型，以及具有不同特征(例如，性别、体重)的个人内部和之间的模型 在可能影响模型拟合的可变条件下(例如，湿度、心率)。我们将创建一个数据同化 软件系统，Brac Estimator软件，它整合了所有可用的数据，以产生最大 精确的eBrAC测量。软件输出将包括连续饮酒事件的识别 EBrAC信号和eBrAC总分(例如，eBrAC峰值、eBrAC峰值时间、饮酒面积 曲线)和置信度带。该软件将是平台可移植的，可以单独运行或集成到其他 移动医疗技术或精准医疗协议。这项提议在技术上是创新的 复杂、可行，最终将产生第一个完成TAC-eBrAC转换的工具 使其有可能获得可解释的定量测量的天然酒精消费在 菲尔德。这项研究的预期结果是扩大了TAC生物传感器的实用性，供研究人员、临床医生、 和个人监测自然饮酒并很容易理解结果。

Abstract Transdermal alcohol biosensors offer a promising method for unobtrusively collecting continuous alcohol levels in naturalistic settings over long periods of time. Devices are now available to reliably measure transdermal alcohol concentration (TAC), the amount of alcohol diffusing through the skin, but an often overlooked yet critical issue for making these biosensors valuable is that TAC does not consistently correlate with the easily interpretable measures of breath and blood alcohol concentrations (BrAC/BAC) across individuals, environmental conditions, and devices. The goal of this study is to produce software to convert TAC data into estimates of BrAC/BAC (eBrAC/eBAC). We will meet this goal by 1) developing mathematical models to produce quantitative eBrAC from TAC data, 2) examining alternative options for calibrating these models, 3) testing the model fits using varied types and amounts of very precise data, and 4) packaging the models into a comprehensive data assimilation software program. Specifically, we will enhance the fidelity of the models by integrating advanced physics/physiological-based models with statistical methods and data-driven machine- learning techniques. To reduce the burden currently required to calibrate the models for each individual, we will test a number of calibration procedures, including the replacement of the laboratory alcohol administration session with more varied drinking protocols as well as with population-based parameter estimates. We will test our models and protocols using detailed consumption data collected 1) on two of the investigators, 2) on 40 participants who will each participate in four controlled laboratory drinking sessions, and 3) on 40 participants who will each participate in a field trial and laboratory sessions. We will examine model fits across drinking patterns when using varying amounts of individualized alcohol data (e.g., breath analyzer, drink diary) to calibrate the models, and within and across individuals with differing characteristics (e.g., gender, weight) and under variable conditions (e.g., humidity, heart rate) that may affect model fit. We will create a data assimilation software system, the BrAC Estimator software, that incorporates all available data to produce the most accurate eBrAC measures. The software output will include the identification of drinking episodes, continuous eBrAC signal, and eBrAC summary scores (e.g., peak eBrAC, time of peak eBrAC, area under the drinking curve) with confidence bands. The software will be platform-portable to run alone or to be integrated into other mobile health technologies or precision medicine protocols. This proposal is innovative, technologically sophisticated, and feasible, and would result in the first tool to accomplish the TAC-eBrAC conversion, finally making it possible to obtain interpretable quantitative measurement of naturalistic alcohol consumption in the field. The anticipated result of this study is the expanded utility of TAC biosensors for researchers, clinicians, and individuals to monitor naturalistic alcohol consumption and easily understand the results.