The Human Behaviour-Change Project: An artificial intelligence system to answer questions about changing behaviour.

Changing behaviour is necessary to address many of the threats facing human populations.  However, identifying behaviour change interventions likely to be effective in particular contexts as a basis for improving them presents a major challenge. The Human Behaviour-Change Project harnesses the power of artificial intelligence and behavioural science to organise global evidence about behaviour change to predict outcomes in common and unknown behaviour change scenarios

Outcome Prediction from Behaviour Change Intervention Evaluations using a Combination of Node and Word Embedding

Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project

Peer reviewe

Personal privacy and online systems

A significant portion of the modern internet is funded by commercial return from customised content such as advertising where user interests are learned from users\u27 online behaviour and used to display personalised content. Privacy becomes a concern when personalisation reveals evidence of learning about sensitive topics a user would rather keep private. Examples of potentially sensitive topics we consider include health, finance and sexual orientation. In this thesis we develop novel technologies allowing users to improve control over their personal privacy. We consider three aspects of privacy protection here: i) detecting evidence of unwanted profiling, ii) assessing the potential impact of a threat, and, iii) a flexible framework to help users to take control the flow of information used in personalisation. We model online systems as black-box adversaries with unknown internal workings but with an objective to maximise commercial utility. In a black-box environment absolute measures of privacy are problematic and so our formalism builds on a notion of privacy relative to a baseline. The relative models we develop have the advantage of being learn-able from observation of the black-box system and so can be readily implemented as practical technologies for privacy threat detection, analysis and privacy defence which we validate against data from well-known, real-world online systems

Challenges in the interpretation of colorectal indocyanine green fluorescence angiography: Video vignette

Colorectal anastomotic leakage remains a serious complication with implications on hospital stay, oncological outcomes(1) and treatment cost. (2) Traditional intra-operative visual perfusion assessment has been shown to be suboptimal(3) and surgeons are looking to indocyanine green (ICG) fluorescence angiography as an adjunct to clinical judgement

Practical perfusion quantification in multispectral endoscopic video: using the minutes after ICG administration to assess tissue pathology

The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculatures, intravenously administered ICG perfuses through cancerous tissues differently. We investigate here how a few characteristic values derived from the time series of fluorescence can be used in simple machine learning algorithms to distinguish benign lesions from cancers. These features capture the initial uptake of ICG in the colon, its peak fluorescence, and its early wash-out. By using simple, explainable algorithms we demonstrate, in clinical cases, that sensitivity (specificity) rates of over 95% (95%) for cancer classification can be achieved

BCTTv1 Workshop 2021

Presentations from Susan Michie and Robert West as part of a workshop on the Behaviour Change Techniques Taxonomy (BCTTv1), delivered in the 2021 Strengthening Causal Inference in Behavioral Obesity Research short course at the Indiana University School of Public Health