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一种用于评估南非三个地点与感染艾滋病毒相关的风险行为的机器引导工具的效用:现场评估方案

Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation.

作者信息

Majam Mohammed, Phatsoane Mothepane, Hanna Keith, Faul Charles, Arora Lovkesh, Makthal Sarvesh, Kumar Akhil, Jois Kashyap, Lalla-Edward Samanta Tresha

机构信息

Ezintsha, Faculty of Health Sciences, University of Witswatersrand, Johannesburg, South Africa.

IPRD Solutions, New York, NY, United States.

出版信息

JMIR Res Protoc. 2021 Dec 2;10(12):e30304. doi: 10.2196/30304.

Abstract

BACKGROUND

Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score.

OBJECTIVE

The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV.

METHODS

In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants' risk assessment scores as "predictions" and the test results as the "ground truth." Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool.

RESULTS

Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool's performance and usability, together with user satisfaction and recommendations for improvement.

CONCLUSIONS

Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV.

TRIAL REGISTRATION

South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30304.

摘要

背景

移动技术有助于推进健康项目,研究表明,自动化风险预测模型可成功用于识别感染人类免疫缺陷病毒(HIV)可能性高的患者。机器引导工具是一种算法,它从一份简单问卷中获取一系列主观和客观答案,并计算HIV风险评估分数。

目的

本研究的主要目的是确定机器学习可用于开发机器引导工具,并使我们对某些行为模式与HIV之间的相关性有更深入的统计学理解。

方法

在南非的三个研究地点(两个半农村地区和一个城市地区)各招募200名HIV阴性成年个体。研究过程将包括:(1)在应用系统上独立完成一系列问题(人口统计学、性行为和病史、个人情况、生活方式及症状)(仅在用户请求时提供帮助);(2)护士/咨询师根据南非国家指南进行两次HIV检测(每次研究访视进行一次)(以评估该工具的预测准确性);(3)传达检测结果并完成用户体验调查问卷。本研究的输出指标将通过将参与者的风险评估分数用作“预测值”,将检测结果用作“基本事实”来计算。在第1次访视后进行分析,然后在第2次访视后再次进行分析。所有风险评估分数将用于计算机器引导工具的可靠性。

结果

2020年8月20日获得了威特沃特斯兰德大学人类研究伦理委员会(HREC;伦理参考编号:200312)的伦理批准。本研究正在进行中。数据收集已开始,预计于2021年下半年完成。我们将报告机器引导工具的性能和可用性,以及用户满意度和改进建议。

结论

机器引导的风险评估工具可为大规模HIV筛查提供一种经济高效的替代方案,并有助于提供有针对性的咨询和检测,以防止HIV传播。

试验注册

南非国家临床试验注册中心DOH-27-042021-679;https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545。

国际注册报告识别号(IRRID):DERR1-10.2196/30304。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed83/8686409/955a48403f23/resprot_v10i12e30304_fig1.jpg

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