Towards automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review.

Systematic reviews (SRs) constitute a critical foundation for evidence-based decision-making and policy formulation across various disciplines, particularly in healthcare and beyond. However, the inherently rigorous and structured nature of the SR process renders it laborious for human reviewers. Moreover, the exponential growth in daily published literature exacerbates the challenge, as SRs risk missing out on incorporating recent studies that could potentially influence research outcomes. This pressing need to streamline and enhance the efficiency of SRs has prompted significant interest in leveraging Artificial Intelligence (AI) techniques to automate various stages of the SR process. This review paper provides a comprehensive overview of the current AI methods employed for SR automation, a subject area that has not been exhaustively covered in previous literature. Through an extensive analysis of 52 related works and an original online survey, the primary AI techniques and their applications in automating key SR stages, such as search, screening, data extraction, and risk of bias assessment, are identified. The survey results offer practical insights into the current practices, experiences, opinions, and expectations of SR practitioners and researchers regarding future SR automation. Synthesis of the literature review and survey findings highlights gaps and challenges in the current landscape of SR automation using AI techniques. Based on these insights, potential future directions are discussed. This review aims to equip researchers and practitioners with a foundational understanding of the basic concepts, primary methodologies and recent advancements in AI-driven SR automation, while guiding computer scientists in exploring novel techniques to further invigorate and advance this field

Living analytics methods for the social web

[no abstract

Using Learning to Rank Approach to Promoting Diversity for Biomedical Information Retrieval with Wikipedia

In most of the traditional information retrieval (IR) models, the independent relevance assumption is taken, which assumes the relevance of a document is independent of other documents. However, the pitfall of this is the high redundancy and low diversity of retrieval result. This has been seen in many scenarios, especially in biomedical IR, where the information need of one query may refer to different aspects. Promoting diversity in IR takes the relationship between documents into account. Unlike previous studies, we tackle this problem in the learning to rank perspective. The main challenges are how to find salient features for biomedical data and how to integrate dynamic features into the ranking model. To address these challenges, Wikipedia is used to detect topics of documents for generating diversity biased features. A combined model is proposed and studied to learn a diversified ranking result. Experiment results show the proposed method outperforms baseline models

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories