Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Internet Computing
Systematic literature reviews in software engineering - A systematic literature review
Information and Software Technology
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A study on optimal parameter tuning for Rocchio text classifier
ECIR'03 Proceedings of the 25th European conference on IR research
Exploiting the systematic review protocol for classification of medical abstracts
Artificial Intelligence in Medicine
Screening nonrandomized studies for medical systematic reviews: A comparative study of classifiers
Artificial Intelligence in Medicine
Automatic retrieval of current evidence to support update of bibliography in clinical guidelines
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Medical systematic reviews answer particular questions within a very specific domain of expertise by selecting and analysing the current pertinent literature. As part of this process, the phase of screening articles usually requires a long time and significant effort as it involves a group of domain experts evaluating thousands of articles in order to find the relevant instances. Our goal is to support this process through automatic tools. There is a recent trend of applying text classification methods to semi-automate the screening phase by providing decision support to the group of experts, hence helping reduce the required time and effort. In this work, we contribute to this line of work by performing a comprehensive set of text classification experiments on a corpus resulting from an actual systematic review in the area of Internet-Based Randomised Controlled Trials. These experiments involved applying multiple machine learning algorithms combined with several feature selection techniques to different parts of the articles (i.e., titles, abstract, or both). Results are generally positive in terms of overall precision and recall measurements, reaching values of up to 84%. It is also revealing in terms of how using only article titles provides virtually as good results as when adding article abstracts. Based on the positive results, it is clear that text classification can support the screening stage of medical systematic reviews . However, selecting the most appropriate machine learning algorithms, related methods, and text sections of articles is a neglected but important requirement because of its significant impact to the end results.