AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging.

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.

Knowledge Extraction and Prediction from Behavior Science Randomized Controlled Trials: A Case Study in Smoking Cessation.

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.

Information Extraction of Behavior Change Intervention Descriptions.

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.

Unsupervised Information Extraction from Behaviour Change Literature.

This paper describes our approach to construct a scalable system for unsupervised information extraction from the behaviour change intervention literature. Due to the many different types of attribute to be extracted, we adopt a passage retrieval based framework that provides the most likely value for an attribute. Our proposed method is capable of addressing variable length passage sizes and different validation criteria for the extracted values corresponding to each attribute to be found.