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We propose an automated approach to rank the most salient variables related to a certain clinical phenomenon from scientific literature. Our solution is an automated approach to improve the efficiency of the collection of different health-related measures from a population, and to accelerate the discovery of novel associations and dependencies between health-related concepts.

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Background  Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods  Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results  The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions  While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

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Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system's architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system's performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical 'grand challenges' pursued by the AI research community over the past few decades. We suggest that such challenges lie in the 'comfort zone' of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress.

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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.

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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.

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Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.

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We describe an information extraction (IE) approach for knowledge base population of behavior change scientific intervention findings. In this paper, we focus on building a system able to characterize the specific intervention techniques that are undertaken within behavior change intervention studies. We have investigated three different configurations of a general information retrieval based framework for information extraction: a) an unsupervised approach that hinges on specification of a query for each attribute to be extracted and a few parameters for rule-based post-processing; b) a semi-supervised approach, which uses a part of the ground-truth annotations as a training set to automatically learn optimal representation of the queries; and c) a supervised approach that replaces the rule-based post processing by a text classifier. To train and evaluate our system, we make use of a ground-truth data set annotated by behavior science experts. This dataset consists of a total of 226 research papers on smoking cessation.