A probabilistic similarity metric for Medline records: a model for author name disambiguation

Abstract

We present a model for automatically generating training sets and estimating the probability that a pair of Medline records sharing a last and first name initial are authored by the same individual, based on shared title words, journal name, co-authors, medical subject headings, language, and affiliation, as well as distinctive features of the name itself (i.e., presence of middle initial, suffix, and prevalence in Medline).

Figure 1

Individual attributes’ similarity distributions estimated by comparing the matchsets versus the non-matchsets.