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The online question answering (QA) community has been popular in recent years. In this paper, we focus on the online health question answering (HQA) community. The HQA community provides a platform for health consumers to inquire about health information. There are two ways to use this platform. One is to post a question and wait for answers to be provided by authenticated doctors. The other is to search for relevant questions with answers. For the latter, health consumers may prefer an accepted answer marked by the previous health consumer. However, there is a large proportion of questions without an accepted answer and it is inconvenient for people who want to search for relevant questions. To address this issue, we aim to select high-quality answers from the answers without marked accepted answers. We propose a deep learning approach to achieve this goal. To train the model for the prediction of answer quality, we first view the accepted answer as the positive answer and propose a method to label the negative answer. Next, we capture the semantic information on the question and the answer by the deep learning structure. We then combine the information to predict the quality score of the answer. We collect data from one of the biggest Chinese HQA community and divide them into groups by the medical departments for detailed analysis. Finally, we conduct experiments to show the effectiveness of categorization and the labeling method. The results show that our approach outperforms other studies and we further research into the differences among the results of different categories.

OBJECTIVE: Conventional ultrafiltration (CUF) during cardiopulmonary bypass (CPB) serves to hemoconcentrate blood volume to avoid allogeneic blood transfusions. Previous studies have determined weight-indexed CUF volumes as a continuous variable are associated with postoperative AKI after cardiac surgery but optimal weight-indexed volumes that predict AKI have not been described. DESIGN: Retrospective cohort SETTING: Single-center university hospital PARTICIPANTS: 1,641 consecutive patients that underwent elective cardiac surgery between June 2013-December 2015. INTERVENTIONS: CUF volume was removed during CPB in all participants as part of our routine practice. We investigated the association of dichotomized weight indexed CUF volume removal with postoperative AKI development to provide pragmatic guidance for clinical practice at our institution. MEASUREMENTS AND MAIN RESULTS: Primary outcomes of postoperative AKI was defined by the KDIGO staging criteria and dichotomized, weight indexed CUF volumes (ml/kg) were defined by (i) extreme quartiles (Q3) and by (ii) Youden’s criterion that best predicted AKI development. Multivariable logistic regression models were developed to test the association of these dichotomized indices with AKI status. Postoperative AKI occurred in 827 patients (50.4%). Higher CUF volumes were associated with AKI development by quartiles (CUF >Q(3)=32.6 vs. CUF 32 mL/kg increased the risk for postoperative AKI development. Importantly, CUF volume removal of any amount did not mitigate allogeneic blood transfusion during elective cardiac surgery. Prospective studies are needed to validate these findings.

Objectives To determine whether precardiopulmonary bypass (CPB) normalization of antithrombin levels in infants to 100% improves heparin sensitivity and anticoagulation during CPB and has beneficial effects into the postoperative period. Design Randomized, double-blinded, placebo-controlled prospective study. Setting Multicenter study performed in 2 academic hospitals. Participants The study comprised 40 infants younger than 7 months with preoperative antithrombin levels Interventions Antithrombin levels were increased with exogenous antithrombin to 100% functional level intraoperatively before surgical incision. Measurements and Main Results Demographics, clinical variables, and blood samples were collected up to postoperative day 4. Higher first post-heparin activated clotting times (sec) were observed in the antithrombin group despite similar initial heparin dosing. There was an increase in heparin sensitivity in the antithrombin group. There was significantly lower 24-hour chest tube output (mL/kg) in the antithrombin group and lower overall blood product unit exposures in the antithrombin group as a whole. Functional antithrombin levels (%) were significantly higher in the treatment group versus placebo group until postoperative day 2. D-dimer was significantly lower in the antithrombin group than in the placebo group on postoperative day 4. Conclusion Supplementation of antithrombin in infants with low antithrombin levels improves heparin sensitivity and anticoagulation during CPB without increased rates of bleeding or adverse events. Beneficial effects may be seen into the postoperative period, reflected by significantly less postoperative bleeding and exposure to blood products and reduced generation of D-dimers.

Mining frequent sequences in large databases has been an important research topic. The main challenge of mining frequent sequences is the high processing cost due to the large amount of data. In this paper, we propose a novel strategy to find all the frequent sequences without having to compute the support counts of non-frequent sequences. The previous works prune candidate sequences based on the frequent sequences with shorter lengths, while our strategy prunes candidate sequences according to the non-frequent sequences with the same lengths. As a result, our strategy can cooperate with the previous works to achieve a better performance. We then identify three major strategies used in the previous works and combine them with our strategy into an efficient algorithm. The novelty of our algorithm lies in its ability to dynamically switch from a previous strategy to our new strategy in the mining process for a better performance. Experiment results show that our algorithm outperforms the previous ones under various parameter settings.

It is foreseen that more and more music objects in symbolic format and multimedia objects, such as audio, video, or lyrics, integrated with symbolic music representation (SMR) will be published and broadcasted via the Internet. The SMRs of the flowing songs or multimedia objects will form a music stream. Many interesting applications based on music streams, such as interactive music tutorials, distance music education, and similar theme searching, make the research of content-based retrieval over music streams much important. We consider multiple queries with error tolerances over music streams and address the issue of approximate matching in this environment. We propose a novel approach to continuously process multiple queries over the music streams for finding all the music segments that are similar to the queries. Our approach is based on the concept of n-grams, and two mechanisms are designed to reduce the heavy computation of approximate matching. One mechanism uses the clustering of query n-grams to prune the query n-grams that are irrelevant to the incoming data n-gram. The other mechanism records the data n-gram that matches a query n-gram as a partial answer and incrementally merges the partial answers of the same query. We implement a prototype system for experiments in which songs in the MIDI format are continuously broadcasted, and the user can specify musical segments as queries to monitor the music streams. Experiment results show the effectiveness and efficiency of the proposed approach.

Since tree-structured data such as XML files are widely used for data representation and exchange on the Internet, discovering frequent tree patterns over tree-structured data streams becomes an interesting issue. In this paper, we propose an online algorithm to continuously discover the current set of frequent tree patterns from the data stream. A novel and efficient technique is introduced to incrementally generate all candidate tree patterns without duplicates. Moreover, a framework for counting the approximate frequencies of the candidate tree patterns is presented. Combining these techniques, the proposed approach is able to compute frequent tree patterns with guarantees of completeness and accuracy.

Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded.

In this paper, we propose a novel representation of sequences based on the structural information of the sequences. A sequence is represented by a set of rules, which are derived from its subsequences. There are two types of subsequences of interest. One is called frequent pattern, which is a subsequence appearing often enough in the sequence. The other is called correlative pattern, which is a subsequence composed of highly correlated elements. The rules derived from the frequent patterns are called frequent rules, while the ones derived from the correlative patterns are called correlative rules. By considering music objects as sequences, we represent each of them as a set of rules and design a similarity function for effective music retrieval. The experimental results show that our approaches outperform the approaches based on the Markov-model on the average precision.

With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.

The dramatic growth of the Web has brought about the rapid accumulation of data and the increasing possibility of information sharing. As the population on the Web grows, the analysis of user interests and behaviors will provide hints on how to improve the quality of service. We define user interests and behaviors based on the documents read by the user. A method for mining such user interests and behaviors is then presented. In this way, each user is associated with a set of interests and behaviors, which is stored in the user profile. In addition, we define six types of user profiles and a distance measure to classify users into clusters. Finally, three kinds of recommendation services using the clustered results are realized. For performance evaluation, we implement these services on the Web to make experiments on real data/users. The results show that the average acceptance rates of these services range from 71.5% to 94.6%.

Searching information from the WWW efficiently and effectively has become a very important issue. We apply the information filtering concept to finding good matches of Web pages to what the user needs. Furthermore, we propose four new methods for indexing user information needs and show their efficiency in comparison with two well-known approaches.

Building a system for the users on the Internet to search interested resources or to provide guided browsing services has formed an important trend as a result of the rapid accumulation of information, the booming development of information system and the increasing requirement of resource sharing. In this paper, we present the Integrated resource Query and guidedBrowsing System (IQBS), which has been implemented at our laboratory, to achieve this goal. The scheme we proposed takes advantage of the strong power of the database system to structure and query data, and greatly promote the qua!ity and performance of searching resources in Internet. In addition, mechanisms are designed to guide the users on the Internet to conveniently browse resources with a reduced traffic cost. Keywords: Internet resource discovery, World wide web, Query processing, Rough set, Guided browsing, Supervised learning, Hypertext management.