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      The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation

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          Abstract

          Background

          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?’ .

          Methods

          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.

          Discussion

          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.

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          Most cited references19

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap

            Julian Elliott and colleagues discuss how the current inability to keep systematic reviews up-to-date hampers the translation of knowledge into action. They propose living systematic reviews as a contribution to evidence synthesis to enhance the accuracy and utility of health evidence.
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              Natural language processing

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                Author and article information

                Contributors
                s.michie@ucl.ac.uk
                James.thomas@ucl.ac.uk
                m.johnston@abdn.ac.uk
                aonghusa@ie.ibm.com
                jst@cs.ucl.ac.uk
                mk744@medschl.cam.ac.uk
                lea.deleris@ie.ibm.com
                a.finnerty@ucl.ac.uk
                marta.marques@ucl.ac.uk
                emma.norris@ucl.ac.uk
                a.o'mara-eves@ucl.ac.uk
                robert.west@ucl.ac.uk
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central (London )
                1748-5908
                18 October 2017
                18 October 2017
                2017
                : 12
                : 121
                Affiliations
                [1 ]ISNI 0000000121901201, GRID grid.83440.3b, UCL Centre for Behaviour Change, , University College London, ; 1-19 Torrington Place, London, WC1E 7HB UK
                [2 ]ISNI 0000000121901201, GRID grid.83440.3b, EPPI-Centre, Department of Social Science, , University College London, ; London, UK
                [3 ]ISNI 0000 0004 1936 7291, GRID grid.7107.1, Health Psychology, , University of Aberdeen, ; Scotland, UK
                [4 ]GRID grid.424816.d, IBM Research – Ireland, ; Dublin, Ireland
                [5 ]ISNI 0000000121901201, GRID grid.83440.3b, Department of Computer Science, , UCL, ; London, UK
                [6 ]ISNI 0000000121885934, GRID grid.5335.0, Primary Care Unit, Institute of Public Health, , University of Cambridge, ; Cambridge, UK
                [7 ]ISNI 0000000121901201, GRID grid.83440.3b, Department of Epidemiology and Public Health, , University College London, ; London, UK
                Article
                641
                10.1186/s13012-017-0641-5
                5648456
                29047393
                d709f53b-a470-43f3-8df8-53445630b00f
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 August 2017
                : 28 August 2017
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2017

                Medicine
                behaviour change interventions,implementation,ontology,machine learning,natural language processing,evidence synthesis,artificial intelligence

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