METHOD: A mixed method design incorporating quantitative and qualitative data was used to increase credibility, validity and comprehensiveness of the results. Thirty-eight hospitals (Malaysia = 21, Queensland = 17) participated in Phase 1 (quantitative component) of the study involving completion of an infrastructure checklist by a speech-language pathologist from each hospital regarding availability of networking and communication, staffing and financial support, facilities and documentation of guidelines for dysphagia management. Subsequently, eight sub-samples from each cohort were then involved in Phase 2 (qualitative component) of the study involving a semi-structured interview on issues related to the impact of infrastructure availability or constraints on service provision.
RESULT: The current study reveals that multiple challenges exist with regard to dysphagia services in Malaysian government hospitals compared to Queensland public hospitals.
CONCLUSION: Overall, it was identified that service improvement in Malaysia requires change at a systems and structures level, but also, more importantly, at the individual/personal level, particularly focusing on the culture, behaviour and attitudes among the staff regarding dysphagia services.
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.