A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates

Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates. We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov. We tested a matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations. The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews. The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 and recall@100 of 60.9%, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline. In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%). The proposed method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates. The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update.Comment: Journal of Biomedical Informatics Vol. 79, March 2018, p. 32-4

Recent developments in Remote Document Supply (RDS) in the UK – 3

A review of recent developments in remote document supply and related matters in the UK. With the decline in remote document supply the future participation of a key institution is called into question. While there are few other realistic options, the two leading alternatives are engaged in a battle for the same market. Furthermore, the future of a key standard underpinning transactions is also uncertain

Empirical Study of Deep Learning for Text Classification in Legal Document Review

Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data

Human Trafficking in Europe: An Economic Perspective

Based on a document originally prepared for the Eleventh Economic Forum of the Organization for Economic Security and Cooperation in Europe, held in Prague between 20-23 May 2003. Attempts to comprehend and document human trafficking’s underlying economic dimensions, and places the concerns of trafficking within broader migration analysis (including the role of irregular migration). It also comments on the financial flows involved in trafficking, and on the different patterns of financing trafficking services. Further, it contains a brief review of the evidence, as to the extent to which organized crime is involved in human trafficking