Automated Extraction of Number of Subjects in Randomised Controlled Trials

06/22/2016
by   Abeed Sarker, et al.
0

We present a simple approach for automatically extracting the number of subjects involved in randomised controlled trials (RCT). Our approach first applies a set of rule-based techniques to extract candidate study sizes from the abstracts of the articles. Supervised classification is then performed over the candidates with support vector machines, using a small set of lexical, structural, and contextual features. With only a small annotated training set of 201 RCTs, we obtained an accuracy of 88%. We believe that this system will aid complex medical text processing tasks such as summarisation and question answering.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/11/2018

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

We present a corpus of 5,000 richly annotated abstracts of medical artic...
research
07/04/2016

A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism

Community-based Question Answering (CQA) sites play an important role in...
research
04/20/2013

Analytic Feature Selection for Support Vector Machines

Support vector machines (SVMs) rely on the inherent geometry of a data s...
research
07/09/2015

FAQ-based Question Answering via Word Alignment

In this paper, we propose a novel word-alignment-based method to solve t...
research
10/09/2018

Answer Extraction in Question Answering using Structure Features and Dependency Principles

Question Answering (QA) research is a significant and challenging task i...
research
04/24/2020

Target specific mining of COVID-19 scholarly articles using one-class approach

In recent years, several research articles have been published in the fi...

Please sign up or login with your details

Forgot password? Click here to reset