The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received.
The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations
accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications
within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that
it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features
using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and
their statistical analysis provides greater insight into citation behavior.