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.
Historiography Semantics Publications
Zdenko Kasá(c) Stefan Schulz
Institute for Medical Informatics,Statistics and Documentation,Medical University of Graz,Austria;Fa Institute for Medical Informatics,Statistics and Documentation,Medical University of Graz,Austria
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
437-441
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）