Medical Literatures Query-Tuning

The number of medical literature as the popularity of computer and network grow exponentially , the number of medical literature end of 1990 from 676 million in the MEDLINE / PubMed and in 2011 has now accumulated more than 20 million medical literature. For busy medical researcher and physician, from this huge database to search for literature is a major burden.
To complement the work of the user''s search, the search engine intervention is necessary. However, PubMed provides the default search strategy is not an effective way to return relevant results, inexperienced users need to constantly try and error to find the related article, and the lack of good sorting mechanism.
This study tried to implement a system, the user can according keywords to queries, also you can type a sentence and even articles do search through the user interaction with the system interface to find the related articles, and provides sort of mechanism by relevance to help user find the articles quickly. Also in the system for real-time classification of medical literature using PICO classifier for the classification of sentences to help users read faster when user got the target sentence. Finally, in the experimental results and discussion we collection the clearly described Patients, Intervention, Outcome-related sentences as the experimental materials, experimental materials are divided into training data and test data, the training data through the NLTK Bayesian classifier to construct three classification model and through the PICO classification algorithms to classify the test data by 10-fold cross validation for assessing the performance of PICO.