The Human Behaviour-Change Project

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Octavia Richard
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1 The Human Behaviour-Change Project Participating organisations A Collaborative Award funded by the

2 This evening Opening remarks from the chair Mary de Silva, The Wellcome Trust 1. The need and vision Susan Michie, UCL 2. The preliminary ontology Robert West, UCL 3. The sciences working together Marie Johnston, Aberdeen University Question and answer session 4. Automated data extraction from papers Pól Mac Aonghusa, IBM Research 5. Machine learning John Shawe-Taylor, UCL 6. The querying system for users James Thomas, UCL 7. The future Mike Kelly, Cambridge University Question and answer session and discussion

3 The Vision Susan Michie Professor of Health Psychology Centre for Behaviour Change, UCL

4 Building the science of behaviour change The HBCP aims to revolutionise the ways in which we Build knowledge and understanding about behaviour change Use that knowledge to answer real-world questions What works, compared with what, how well, with what exposure, with what behaviours, for whom, in what settings and why?

5 Building the science of behaviour change The HBCP aims to revolutionise the ways in which we Build knowledge and understanding about behaviour change Use that knowledge to answer real-world questions The method Harness cutting edge methodologies, to build 1. An Ontology of Behaviour Change Interventions 2. An Artificial Intelligence System, including Natural Language Processing and Machine Learning 3. A User Interface including a query system Collaboration between behavioural, computer and information science

6 The problem Volume of research >200 evaluations of behavioural interventions published each day Reporting variability Studies are reported very variably so difficult to synthesise or to draw theoretical conclusions about mediation and moderation of effects Context sensitivity Much literature not directly relevant to specific contexts of knowledge users Need for timeliness Typical time for study results to be included in systematic reviews years

7 The need Collaboration with computer science to efficiently advance our understanding of behaviour to answer questions from policy makers & practitioners

8 The 4-year plan 1. A Behaviour Change Intervention Ontology for organising relevant information from research reports 2. A Natural Language Processing system to find and extract that information starting with the use case of smoking cessation 3. Machine Learning and Reasoning Systems that integrate and extrapolate from that information to generate new knowledge and hypotheses about behaviour change 4. A User Interface that answers questions about behaviour change interventions and explains its conclusions

9 The Preliminary Ontology of Behaviour Change Interventions Robert West Professor of Health Psychology Tobacco and Alcohol Research Group, UCL

10 The big question in behaviour change What works, compared with what, how well, with what degree of exposure, for whom, in what settings with what behaviours, and why?

11 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

12 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

13 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

14 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

15 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

16 Unpacking the big question Intervention and comparator What is the behaviour change intervention and how is it delivered? Compared with what? Exposure What is the reach of, and engagement with, the intervention? Population Whose behaviour does the intervention aim to change? Setting What is the setting in which the intervention is operating? Mechanism How does the intervention work? Behaviour What behaviour or behaviours is the intervention targeting?

17 The BCI Ontology v1 West & Michie (2016) A Guide to the Development and Evaluation of Digital Behaviour Change Interventions in Healthcare. London: Silverback

18 Cochrane s PICO Ontology We will map the BCI ontology where possible to Cochrane s PICO Ontology of clinical studies 1 Patient, Population or Problem What are the characteristics of the population and the condition of interest? Intervention What is the intervention under consideration for this patient or population? Comparison What is the alternative to the intervention? Outcome What are the relevant outcomes? 1

19 The Sciences Working Together Marie Johnston Professor of Health Psychology University of Aberdeen

20 Iterative interaction between the sciences Ontology of behaviour change interventions How can we organise the evidence? Making the evidence accessible at scale in real time How can we make the evidence usable? Extracting and interpreting the evidence What does the evidence show? computer science information science behavioural science

21 Iterative interaction between the sciences Ontology of behaviour change interventions How can we organise the evidence? Making the evidence accessible at scale in real time How can we make the evidence usable? Extracting and interpreting the evidence What does the evidence show? computer science information science behavioural science

22 Iterative interaction between the sciences Ontology of behaviour change interventions How can we organise the evidence? Making the evidence accessible at scale in real time How can we make the evidence usable? Extracting and interpreting the evidence What does the evidence show? computer science information science behavioural science

23 Iterative interaction between the sciences Ontology of behaviour change interventions How can we organise the evidence? Making the evidence accessible at scale in real time How can we make the evidence usable? Extracting and interpreting the evidence What does the evidence show? computer science information science behavioural science

24 The collaboration of 3 sciences

25 Iterative interaction between the sciences computer science information science behavioural science

26 The Human Behaviour-Change Project Questions and discussion

27 Knowledge Extraction from Reports Pól Mac Aonghusa Senior Manager of Social, Mobile and Decision Science Research IBM Research Dublin, Ireland

28 The task Can we teach a computer to be our Behaviour Change research assistant? Interpret documents many times faster than a human - large, longterm memory Systematic identification of connections over everything it learns Unbiased assessment of evidence vs opinion and consensus vs disagreement Generate reusable knowledge that both humans and machine can interpret. to answer real-world questions

29 Challenges Join knowledge from many sources significant effort to identify connections De-noise relevant knowledge useful information represents small proportion of total content Resolve content ambiguity versus precise semantics of ontology Assign confidence to learned knowledge assess evidence versus opinion Connect rich semantic knowledge source to ML & AI without combinatorial meltdown

30 Starting points Medical Recap Watson Oncology Advisor Incorporating human faculties such as Debating

31 The Role of Machine Learning John Shawe-Taylor Professor of Computer Science Centre for Computational Statistics and Machine Learning, UCL

32 Artificial Intelligence (AI) Can computers be programmed to show human levels of intelligence? AI has been a dream of Computer Scientists since the birth of automated computation, e.g., Ada Lovelace, Alan Turing First attempts at creating AI were focused on reproducing logical reasoning in automatic programs Despite some successes (e.g., Deep Blue chess-playing system) reducing intelligence to logic alone leads to a combinatorial explosion of possibilities that defeats even the fastest machines General purpose AI seemed as remote as ever

33 Machine Learning (ML) ML develops algorithms to find patterns in data: based on probabilistic analysis rather than logical inference Simplest ML tasks are supervised learning: data such as images labelled with content (e.g., contains bicycle ) Task is to feed this data to an algorithm that outputs a function to classify new images (i.e., image contains/doesn t contain a bicycle) There have been significant advances in solving these types of problems: Support Vector Machines (SVMs), boosting and deep learning are able to give accuracies similar to humans

34 ML 4 AI Turning an AI problem into a logical task can throw the baby out with the bathwater: Richer representations have been shown to retain semantic information, e.g., in natural language processing Furthermore the additional information contains patterns that machine learning can for example use to learn to shortcut the combinatorial explosion Remarkable successes such as the IBM Watson (playing Jeopardy), DeepMind (playing Atari games and Go) etc. HBCP will leverage these latest approaches to learn to populate ontologies and suggest improvements to the structures

35 The user interface: aims and evaluation James Thomas Professor of Social Research & Policy EPPI-Centre, UCL

36 The user interface Outputs of machine / human effort will be made available in an online portal with two main aims: To enable widespread use of the knowledge generated To feed back into the AI system and the ontology a wide perspective of views Two evaluations will be conducted: Which parts of a systematic review can be automated using the new system (and how well)? Can the system transform the nature of evidence synthesis in terms of the types of evidence utilised and the inferences developed?

37 The Future: Science and its Application Mike Kelly Senior Visiting Fellow Institute of Public Health, University of Cambridge

38 Science Putting behaviour change on a properly evidence-based platform Policy problems and behaviour and behaviour change The current gap between knowledge and action The possibility of superseding common sense! Making scientific sense of mega information A new revolution in the evidence base

39 Applications Policy makers in public health and beyond providing a platform for evidence based policy interventions National public health bodies Public Health England, Agency for Health Care Research and Quality (AHRQ) USA The What Works Collaborations. National Institute of Health and Care Excellence (NICE) Academic colleagues Cochrane/Campbell Collaborations Information Scientists

40 The Human Behaviour-Change Project Questions and discussion

Evaluating behavior change interventions in terms of their component techniques. Susan Michie

Evaluating behavior change interventions in terms of their component techniques Susan Michie Professor of Health Psychology Director, Centre for Behaviour Change University College London, UK ISBNPA conference,