A model based on multi-features to enhance healthcare and medical document retrieval

A major problem in biomedical informatics is the contextual retrieval and ranking of medical and healthcare information. In this article, we present a model for extracting semantic relations among medical and clinical documents. The purpose is to maximise contextual retrieval and ranking performance with minimum input from users.

We developed and evaluated a medical search engine that relies on a multi-features similarity model. The indexed documents are represented as a network that reflects the semantic relations among documents to assess topical rankings.

The evaluation measurements include the following: recall, precision and R-precision. We used OHSUMED collection to evaluate our work with runs submitted to TREC-9. We provide a comparison of the top five runs that achieved the highest average precision scores. In addition, we used questionnaire-based evaluation to measure the effectiveness of the ranking task.

The results indicated that the proposed model achieved a higher average precision in comparison with top-scored runs submitted to TREC-9; the improvement of our model over other methods is statistically significant (p-value <0.0001). Furthermore, a questionnaire-based experiment showed that the proposed model performed quite well in ranking retrieved documents according to their topics.