Introducing the Human Behaviour-Change Project (HBCP): Using Machine Learning and Artificial Intelligence to improve behavioural science
By ucjubil, on 11 April 2017
By: Dr Emma Norris, Dr Ailbhe Finnerty and Prof Susan Michie
Human behaviour needs radical change to protect our individual and collective health and well-being. To achieve this, we need to develop more effective behaviour change interventions, tailored to the behaviour, population and setting. The Human Behaviour-Change Project (HBCP) is working to build an Artificial Intelligence system to continually scan the world literature on behaviour change, extract key information, and use this to build and update a model of human behaviour to answer the big question:
‘“What works, compared with what, how well, with what exposure, with what behaviours, for how long, for whom, in what settings and why?”
Many of the global threats we face, for example, to health and environmental sustainability, can only be solved by people, organisations and governments changing their behaviour. To achieve this we need to rapidly improve our understanding of behaviour and how to change it.
The scientific literature on behaviour change is vast and accumulating at an accelerating rate. However, this literature is fragmented, and often inconsistently and incompletely reported. Current attempts to synthesise this evidence can take years, omit relevant information and fail to detect patterns which enable generalisation of knowledge beyond the contexts the evidence was created.1
Advances in computing such as the IBM Watson platform demonstrate how it is possible to apply Natural Language Processing and Machine Learning technologies to reveal insights from large volumes of unstructured text. For example, Watson for Oncology recommends interventions for individual cancer patients based on clinical evidence extracted and organised from research publications, medical reports and clinical trials.
It is now possible to create Artificial Intelligence systems to identify relevant information in the world literature, extract it into an organised knowledge base (‘ontology’2) created by behavioural scientists, and generate new insights about behaviour change. This knowledge base can then be interrogated on-demand to answer questions about behaviour change, whether big or small. It will provide answers drawing on knowledge integrated from a broader literature than humans can possibly review, indicate relevant references and estimate the confidence with which statements can be made.
The Human Behaviour-Change Project, funded by the Wellcome Trust and led by Professor Susan Michie, is a collaboration between behavioural scientists, computer scientists and information scientists based at University College London and Universities of Cambridge and Aberdeen, UK, and the IBM Research Laboratory in Ireland. A multi-disciplinary team are working over four years to revolutionise current practices of evidence synthesis and our ability to generate new knowledge.
The behavioural scientists are developing an ontology of behaviour change interventions that will organise the fragmented knowledge in the scientific literature into a form that enables the efficient accumulation of knowledge (See Figure). This will provide a unified language for behaviour change interventions and their components, involving the development of new taxonomies and the extension of established taxonomies such as the BCTTv1 for behaviour change techniques.3 The behavioural scientists are also annotating published literature guided by the developing ontology to build a knowledge store.
Top level of the Behaviour Change Intervention Ontology
The computer scientists are building an Artificial Intelligence system, trained by the annotations of the behavioural scientists. This will apply Natural Language Processing to extract relevant information from scientific reports and to organise that information into the Ontology using reasoning and machine learning. The Artificial Intelligence system will furthermore infer new knowledge while continually learning from new information fed into it.
The information scientists will build and evaluate a sophisticated online user interface to interact with the Artificial Intelligence system. This will enable users to readily access the breadth and depth of up-to-date evidence, and get answers to their questions, with explanations of these answers that people can understand and trust.
We look forward to sharing updates during the course of the project.
- Elliott JH, Turner T, Clavisi O, et al. Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap. PLOS Medicine 2014; 11(2): e1001603.
- Larsen KR, Michie S, Hekler EB, et al. Behavior change interventions: the potential of ontologies for advancing science and practice. Journal of Behavioral Medicine 2016: 1-17.
- Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine 2013; 46(1): 81-95.
BIO: Dr Emma Norris is a Research Associate on the Human Behaviour-Change Project. Her experience includes the development, trialling and evaluation of behaviour change interventions, primarily promoting physical activity. Find Emma Norris on Twitter
Dr Ailbhe Finnerty is a Research Associate on the Human Behaviour-Change Project. Her experience includes the investigation of digital traces in understanding human behaviour, the impact of technology on communication and measuring nonverbal behaviour in social interactions.
Prof Susan Michie is the Principal Investigator on the Human Behaviour-Change Project, Professor of Health Psychology and Director of the Centre for Behaviour Change at UCL. Susan’s research focuses on developing the science of behaviour change interventions and applying behavioural science to interventions. She works with a wide range of disciplines, practitioners and policy-makers and holds grants from a large number of organisations including the Wellcome Trust, National Institute of Health Research, Economic and Social Research Council and Cancer Research UK. Find Susan Michie on Twitter
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