  Community-based Question Answering (CQA) sites play an important role in
addressing health information needs. However, a significant number of posted
questions remain unanswered. Automatically answering the posted questions can
provide a useful source of information for online health communities. In this
study, we developed an algorithm to automatically answer health-related
questions based on past questions and answers (QA). We also aimed to understand
information embedded within online health content that are good features in
identifying valid answers. Our proposed algorithm uses information retrieval
techniques to identify candidate answers from resolved QA. In order to rank
these candidates, we implemented a semi-supervised leaning algorithm that
extracts the best answer to a question. We assessed this approach on a curated
corpus from Yahoo! Answers and compared against a rule-based string similarity
baseline. On our dataset, the semi-supervised learning algorithm has an
accuracy of 86.2%. UMLS-based (health-related) features used in the model
enhance the algorithm's performance by proximately 8 %. A reasonably high rate
of accuracy is obtained given that the data is considerably noisy. Important
features distinguishing a valid answer from an invalid answer include text
length, number of stop words contained in a test question, a distance between
the test question and other questions in the corpus as well as a number of
overlapping health-related terms between questions. Overall, our automated QA
system based on historical QA pairs is shown to be effective according to the
data set in this case study. It is developed for general use in the health care
domain which can also be applied to other CQA sites.
