METHODS: For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall.
RESULTS: From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier.
CONCLUSION: Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques.
OBJECTIVES: To conduct a systematic review of RCTs involving topical drugs published in the Archives of Dermatology, Journal of the American Academy of Dermatology and British Journal of Dermatology for correct classification of studies as vehicle versus placebo-controlled.
METHODS: RCTs involving topical drugs published in the Archives of Dermatology, Journal of the American Academy of Dermatology and British Journal of Dermatology from January 1999 to November 2008 were identified through PubMed, supplemented by citation lists from the individual journals' web pages. Only original studies that involved using a topical control or used the term topical "vehicle" or "placebo" were selected. The studies were examined for correct classification as vehicle-controlled, the year of publication, country of origin, sample size, funding source and nature of study center.
RESULTS: Out of 132, 64 (49%) correctly classified their studies as vehicle-controlled. Pharmaceutical-funded studies (55%, P=0.01) were significantly associated with the use of correct classification.
LIMITATIONS: As only three peer-reviewed dermatology journals were studied, findings may not be generalized to other dermatology journals and other types of publications.
CONCLUSION: This systematic review highlights a common pitfall in the reporting of studies of topical dermatology drugs.