  Keeping track of translational research is essential to evaluating the
performance of programs on translational medicine. Despite several indicators
in previous studies, a consensus measure is still needed to represent the
translational features of biomedical research at the article level. In this
study, we first trained semantic representations of biomedical entities and
documents (i.e., bio entity2vec and bio doc2vec) based on over 30 million
PubMed articles. With these vectors, we then developed a new measure called
Translational Progression (TP) for tracking biomedical articles along the
translational continuum. We validated the effectiveness of TP from two
perspectives (Clinical trial phase identification and ACH classification),
which showed excellent consistency between TP and other indicators. Meanwhile,
TP has several advantages. First, it can track the degree of translation of
biomedical research dynamically and in real time. Second, it is straightforward
to interpret and operationalize. Third, it doesn%u2019t require labor-intensive
MeSH labeling and it is suitable for big scholarly data as well as papers that
are not indexed in PubMed. In addition, we examined the translational
progressions of biomedical research from three dimensions (including overall
distribution, time, and research topic), which revealed three significant
findings. The proposed measure in this study could be used by policymakers to
monitor biomedical research with high translational potential in real time and
make better decisions. It can also be adopted and improved for other domains,
such as physics or computer science, to assess the application value of
scientific discoveries.
