New article on Text Mining the History of Medicine


We are pleased to announce the publication of a new article in PLOS ONE describing the development of new TM resources and tools aimed at allowing the extraction of various types of semantic information from published historical medical documents, dating back to the mid-19th century. The resulting TM pipeline has been applied to two large archives of published historical documents, and the semantically enriched archives have been used as the basis for the development of a semantically-enriched search system that provides facilities for efficient exploration of the archives.

Paul Thompson, Riza Theresa Batista-Navarro, Georgios Kontonatsios, Jacob Carter, Elizabeth Toon, John McNaught, Carsten Timmermann, Michael Worboys and Sophia Ananiadou (2016). Text Mining the History of Medicine. PLoS ONE 11(1): e0144717.


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.

Resource Availability

The two new resources developed are available from the META-SHARE network of language repositories.
The time-sensitive inventory of medical terminological inventory is available here.
The HIMERA annotated corpus is available here.

Semantic search interface

The History of Medicine (HOM) semantic search interface is avaiable here.

Futher Information

For more information about the Mining the History of Medicine project, please see the homepage of the project.

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