Automated Extraction of Number of Subjects in Randomised Controlled Trials

TL;DR

This paper introduces a rule-based and machine learning approach to automatically extract the number of subjects from RCT abstracts, achieving high accuracy with minimal training data.

Contribution

It combines rule-based extraction with supervised SVM classification to accurately identify study sizes in medical abstracts using limited annotated data.

Findings

Achieved 88% accuracy with only 201 training examples.

Effective for aiding medical text summarization and question answering.

Combines rule-based and machine learning techniques for information extraction.

Abstract

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.