The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.
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DOI：
10.1186/s13012-017-0641-5
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发表时间：
2017-10-18
期刊：
影响因子：
--
通讯作者：
West R
中科院分区：
文献类型：
--
作者：
Michie S;Thomas J;Johnston M;Aonghusa PM;Shawe-Taylor J;Kelly MP;Deleris LA;Finnerty AN;Marques MM;Norris E;O'Mara-Eves A;West R
行为改变是应对人类健康和福祉面临的挑战以及促进在卫生政策和实践中吸收研究成果的关键。我们需要更好地利用从行为改变干预（BCI）评估中积累的大量证据，并促进在广泛的背景下吸收这些证据。综合和解释这些证据的任务规模和复杂性，以及提高证据的及时性和可获得性，将需要更多的计算机支持。人类行为改变项目（HBCP）将使用人工智能和机器学习（i）开发和评估一个“知识系统”，该系统自动提取，综合和解释BCI评估报告的结果，以产生有关行为改变的新见解，并提高干预有效性的预测，以及（ii）允许用户，如从业人员，政策制定者和研究人员，容易且有效地查询系统以获得问题“什么起作用，与什么相比，效果如何，有什么曝光，有什么行为（多长时间），对谁，在什么环境下以及为什么？”的变体的答案。HBCP将：a）开发BCI评估及其报告的本体，其将给定目标行为的效应大小与干预内容和递送以及作用机制联系起来，如由暴露、人群和设置所调节的; B）开发和训练自动化特征提取系统以使用该本体来注释BCI评估报告; c）开发和训练机器学习和推理算法，以使用带注释的BCI评估报告来预测行为、干预、人群和环境的特定组合的效果大小; d）建立用于询问和更新知识库的用户和机器接口;以及HBCP旨在彻底改变我们综合、解释和提供行为改变干预措施证据的能力，这些证据是最新的，并根据用户需求和背景量身定制。这将提高证据的有用性，并支持证据的实施。本文的在线版本（10.1186/s13012-017-0641-5）包含补充材料，可供授权用户使用。
Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence. The online version of this article (10.1186/s13012-017-0641-5) contains supplementary material, which is available to authorized users.
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