Profiling Clinical Researchers Effectively using Embeddings and Clustering

Abstract

AbstractEffective researcher profiling is key to support rapid research team formation. We developed a profiling method using (1) widely accessible publication titles, (2) document embedding vector representations to consider background, and (3) both general and specific types of datasets. Our results showed that the most similar researchers have cosine similarities of 0.287/0.258. Our preliminary results can support biomedical informaticians to expedite collaborative clinical studies, enhance research quality, and eventually improve patient healthcare outcomes.