English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Community-based Question Answering (CQA) sites play an important role inaddressing health information needs. However, a significant number of postedquestions remain unanswered. Automatically answering the posted questions canprovide a useful source of information for online health communities. In thisstudy, we developed an algorithm to automatically answer health-relatedquestions based on past questions and answers (QA). We also aimed to understandinformation embedded within online health content that are good features inidentifying valid answers. Our proposed algorithm uses information retrievaltechniques to identify candidate answers from resolved QA. In order to rankthese candidates, we implemented a semi-supervised leaning algorithm thatextracts the best answer to a question. We assessed this approach on a curatedcorpus from Yahoo! Answers and compared against a rule-based string similaritybaseline. On our dataset, the semi-supervised learning algorithm has anaccuracy of 86.2%. UMLS-based (health-related) features used in the modelenhance the algorithm's performance by proximately 8 %. A reasonably high rateof accuracy is obtained given that the data is considerably noisy. Importantfeatures distinguishing a valid answer from an invalid answer include textlength, number of stop words contained in a test question, a distance betweenthe test question and other questions in the corpus as well as a number ofoverlapping health-related terms between questions. Overall, our automated QAsystem based on historical QA pairs is shown to be effective according to thedata set in this case study. It is developed for general use in the health caredomain which can also be applied to other CQA sites.

A Semi-supervised learning approach to enhance health care Community-based Question Answering: A case study in alcoholism