PREDICTION OF ALCOHOL TREATMENT DROP-OUT VIA IVR

该应用程序建议开发和实施交互式语音响应(IVR)计划，以获得120名寻求治疗的患者的自我报告和神经心理数据，这些数据预测在接受治疗6周后酒精治疗退出。这项拟议的研究将把IVR收集的神经心理学数据与之前经过验证的计算机软件获得的数据进行比较。机器学习算法将被应用于IVR数据，以推导出预测治疗中途退出的模型。将使用独立的验证样本来评估这些模型的泛化能力。拟议的商业应用：如果患者在开始治疗后不久就停止治疗，药物滥用治疗的趋势是治疗持续时间延长，强度降低，则不能成功。一种提供有效预后信息的廉价的电话驱动的计算机系统为服务的提供者和付款人提供了在竞争日益激烈的市场中开发和增强更有效的治疗方案的机会。

This application proposes to develop and implement an interactive voice response (IVR) program to obtain self-report and neuropsychological data from 120 treatment-seeking patients that is predictive of alcohol treatment drop-out with six weeks of treatment intake. The proposed research will compare the neuropsychological data collected by IVR to data obtained using computer software that has previously been validated. Machine learning algorithms will be applied to the IVR data to derive models predictive of treatment drop-out. The generalizability of these models will be evaluated using an independent validation sample. PROPOSED COMMERCIAL APPLICATIONS: Substance abuse treatment trends toward increased treatment durations with decreased intensity cannot succeed if patients discontinue treatment soon after initiation. An inexpensive, telephone driven computer system that provided valid prognostic information offers both the providers and payors of services opportunities to develop and enhance more effective treatment programs in an increasingly competitive marketplace.