Analysis of Historical Medical Phenomena Using Large N-Gram Corpora

Historically, numerous indirect references to real world phenomena have been conserved in literature. High-quality libraries of digitized books and their derivatives (like the Google NGram Viewer) have proliferated. These tools simplify the visualization of trends in phrase usage within the collective memory of language groups. A straightforward interpretation of these frequency changes is, however, too simplistic to draw conclusions about the underlying reality because it is affected by several sources of bias. Although these resources have been studied in social sciences and psychology, there is still lack of user-friendly, yet rigorous methods for analysis of phenomena relevant for medicine. We present a methodological framework to study relationships of observable phenomena quantitatively over periods, which span over centuries. We discuss its suitability for knowledge extraction from current and future large-scale, book-derived, n-gram collections.