Throughout life, many of us develop unhealthy habits that may feel nearly impossible to change. To quit smoking, reduce alcohol consumption, eat a healthier diet, or become more physically active requires effort and the right state of mind. A team of behavioural scientists at University College London (UCL) and researchers at IBM Research-Ireland are looking at ways to help people reach these goals and achieve better health behaviour by using AI.
It is challenging to find the right ‘active ingredients’, known as Behaviour Change Techniques (BCTs), to motivate individuals towards healthier habits, help them self-manage their behaviour, and select and create environments conducive to supporting these processes. Examples of such techniques are goal setting, action planning, and self-monitoring. Policymakers are trying to find ways to develop effective programs delivered at individual, community, and population levels. What if AI could be used to help find the best BCTs for a given scenario—ultimately helping us to give up unhealthy habits and develop and sustain healthier behaviours?
To help solve these types of real world problem and assist individuals, practitioners and policymakers to find the best information to help them develop effective interventions and policies, behavioural scientists and system architects at UCL have teamed up with IBM researchers in Dublin to use AI—in particular, natural language processing (NLP) and machine learning—to extract information from behaviour change literature and create an AI system to support the decision-making process in choosing interventions.
Human Behaviour Change Project
Building on the internationally recognized expertise from UCL’s Centre for Behaviour Change and on IBM Research’s leading expertise in AI and NLP, the Human Behaviour Change Project (HBCP) is creating an open-access online ‘Knowledge System’ aimed at helping all those wanting to better understand evidence on behavioural science and make better decisions (e.g., policymakers, practitioners, and researchers). The project is funded by the Wellcome Trust and is a collaboration between computer scientists, behavioural scientists, and system architects. The Knowledge System that is being created will search publication databases to find behaviour change intervention evaluation reports, extract and synthesise the findings, provide answers to questions, and draw inferences about behaviour change which will enable answers to questions for which there is little direct evidence.
UCL behavioural scientists are creating a Behaviour Change Intervention Ontology (BCIO) to organise the evidence using standardised terms for describing key concepts and their relationships and annotating published intervention reports using this ontology. IBM researchers are building algorithms to search, predict, recommend, and explain behaviour change interventions that are likely to be effective for a given scenario. These algorithms are being trained by the annotated research papers. As more manually annotated research papers are used to train the Knowledge System, the algorithms will become more accurate and reliable, and human input will be less needed. The third part of the collaboration is led by the system architects, who are building an online interface to make this behaviour change information easily accessible to humans and other computer programs. These three streams of work are highly iterative with ongoing interaction between the behavioural scientists, computer scientists and system architects.
The AI system for behaviour change
The AI system that IBM Research-Ireland is developing consists of:
- Information extraction algorithms to automatically extract key pieces of information from behavior change intervention evaluation reports, and
- Machine learning and reasoning algorithms to perform inference on the extracted information to generate suggested interventions for specific scenarios, and predictions about the likely outcomes of as-yet-untested interventions.
Both unsupervised and supervised information extraction algorithms are being used to extract information from research reports. The algorithms are defined for each information type based on a common framework. In the unsupervised setting, for each entity, we define a query used to identify passages of text likely to contain the target value (e.g., for target value = age of participants, passages must contain the word participant and age/year/old and must include an integer). Candidate answers are then extracted from each passage based on defined criteria (e.g., for age of participant the candidate answers must be integers) and re-ranked according to their alignment with the query. Rankings are proximity-based (i.e., candidate answers which are found in the text closer to the words used in the query are ranked more highly). The highest ranking answer is selected. In the supervised setting, the query is learnt automatically by a classifier trained on the articles manually annotated by the behavioural science team. These annotations are also used for evaluation.
Information extraction code now open-source
Today IBM Research-Ireland is releasing the first open-source version of their information extraction system on Github (https://github.com/HumanBehaviourChangeProject) together with a Swagger API interface.
While the code itself can be used programmatically by developers interested in building systems for information extraction, the swagger interface can also be used to easily extract information from published papers reporting behaviour change evaluations. This version 0.1 of the information extraction system provides an API for extracting a limited set of data, so ‘proof of principle’. For example, user will be able to upload a pdf of a paper and ask the system to identify which of a set of 10 BCTs were used, some characteristics of the study population (e.g., minimum age, maximum age, mean age), and the outcomes and effects of the interventions.
Grounded in machine learning, the work developed by the IBM Research-Ireland team can also be generalized to other kinds of applications like extracting and reasoning on information for computer science or chemistry papers, as well as to other forms of scientific productions. While we are still continuing to advance the capabilities of our algorithms, the technology has the potential to be made available as a service to help domain experts accelerate and scale their learning process.
Our research continues to push the boundaries of AI in the health domain. Ultimately, this technology may lead to a source of effective behavioral change interventions to assist health professionals, researchers, and policymakers in helping people lead healthier lives.
Tailoring behaviour change interventions to specific groups of people, with certain characteristics, is associated with improved health practices and outcomes, both physical and psychological. This should increase average life spans of these groups of people and may also lead to more general societal benefits. For example, obesity, antimicrobial resistance, and hospital-acquired infections can be mitigated by healthier eating, appropriate antibiotic prescribing, and improved hygiene behaviours, respectively. Thus, advancing the understanding of human behaviour change, at both individual and population levels, can potentially have a wide-ranging impact on shaping the course of human civilization in the coming years.
Unsupervised Information Extraction from Behaviour Change Literature. Debasis Ganguly, Léa A. Deleris, Pol Mac Aonghusa, Alison J. Wright, Ailbhe N. Finnerty, Emma Norris, Marta M. Marques, Susan Michie. IOS Press Vol 247 Pages 680 – 684, 2018
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. Susan Michie, James Thomas, Marie Johnston, Pol Mac Aonghusa, John Shawe-Taylor, Michael P. Kelly, Léa A. Deleris, Ailbhe N. Finnerty, Marta M. Marques, Emma Norris, Alison O’Mara-Eves and Robert West. Implementation Science 12:121, 2017