A semi-supervised learning approach to enhance health care community-based question answering: A case study in alcoholism

Papis Wongchaisuwat*, Diego Klabjan, Siddhartha Reddy Jonnalagadda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Background: 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 Web-based health communities. Objective: 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 Web-based health content that are good features in identifying valid answers. Methods: Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. 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. Results: On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system-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 are 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, and a number of overlapping health-related terms between questions. Conclusions: Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites.

Original languageEnglish (US)
Article numbere24
JournalJMIR Medical Informatics
Issue number3
StatePublished - Jul 2016


  • Consumer health informatics
  • Machine learning
  • Natural language processing
  • Question answering
  • Web-based health communities

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics


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