We are grateful to Professor Cohen for his letter and clarification of the support vector machine algorithm (SVM) results (in press). We agree that the results he supplies fill a gap in our paper. We could not have performed this comparison in our paper, as the first version was written prior to the publication of his own article,1 which in any case, as Dr Cohen points out, did not include the SVM results in terms of within-groups sum of squares (WSS). We would like, however, to be cautious about the broader conclusion from the results in table 1 of Dr Cohen's letter. While they are indeed superior to our factorized version of the complement naïve Bayes (FCNB) approach, they do not necessarily indicate the general superiority of the SVM classifier over the Bayesian methods. Our implementation was an extension of the classical naïve Bayes classifier for the kind of imbalanced data likely to be encountered when classifying abstracts for a systematic review. New exciting developments in the area of Bayesian text classification, such as discriminative multinomial naïve Bayes2 and latent Dirichlet allocation,3 are more than likely to improve our results significantly. This is the topic of our current research. Moreover, we would like to bring up another aspect related to the use of SVM versus Bayesian approaches as regards the running time of the algorithms. It is well known that SVM is significantly slower than the Bayesian methods. While this may be acceptable for the data used in Cohen1 and in Cohen et al4 and in our paper5 which have the order of 103 abstracts, it may not be the case for datasets orders of larger magnitude. However, datasets with the order of 104 abstracts are, as far as we know, common in the practice of systematic review production. When working recently6 with such large datasets we attempted to use SVM, but the running times of the train/test protocols on the computers available to us were unacceptably long. Finally, we want to comment briefly on the performance of FCNB/weight engineering (WE) on the Opioids dataset. As this dataset has a very high imbalance (very low inclusion rate), it is encouraging to see that the FCNB/WE method, which, as we discuss in our paper, has been engineered specifically to work well with such imbalanced data, indeed performs better than the standard SVM. We agree with Professor Cohen that application of the text mining methods developed specifically for imbalanced data is an interesting topic of research (see eg, Zhuang and Dai7). None. Not commissioned; internally peer reviewed.

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.

OBJECTIVE: To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. DESIGN: The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance. MEASUREMENTS: Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. RESULTS: The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. CONCLUSION: The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.

Partial cognates are pairs of words in two languages that have the same meaning in some, but not all contexts. Detecting the actual meaning of a partial cognate in context can be useful for Machine Translation tools and for Computer-Assisted Language Learning tools. In this paper we propose a supervised and a semi-supervised method to disambiguate partial cognates between two languages: French and English. The methods use only automatically-labeled data; therefore they can be applied for other pairs of languages as well. We also show that our methods perform well when using corpora from different domains.