Text Mining the History of Medicine

• Published in: PloS one Vol. 11; no. 1; p. e0144717
• Main Authors: Thompson, Paul, Batista-Navarro, Riza Theresa, Kontonatsios, Georgios, Carter, Jacob, Toon, Elizabeth, McNaught, John, Timmermann, Carsten, Worboys, Michael, Ananiadou, Sophia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.01.2016; Public Library of Science (PLoS)
• Subjects: Accessibility; Analysis; Archives; Archives & records; Bioinformatics; Causality; Computer science; Data Mining; Data processing; Dating techniques; Digitization; Disease; Drugs; History of Medicine; History, 19th Century; Keywords; Medical research; Medical services; Medicine; Queries; Query expansion; Researchers; Science history; Searching; Semantics; Studies; Terminology; Texts; Tuberculosis; Iran; United Kingdom > UK
• DOI: 10.1371/journal.pone.0144717
• ISSN: 1932-6203

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform.