Contextualisation of Biomedical Knowledge Through Large-Scale Processing of Literature, Clinical Narratives and Social Media

  • Goran Nenadic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)


Medicine is often pictured as one of the main examples of “big data science” with a number of challenges and successful stories where data have saved lives [1]. In addition to structured databases that store expert-curated information, unstructured and semi-structured data is a huge and often most up-to-date resource of medical knowledge. These include scientific literature, clinical narratives and social media, which typically capture findings, knowledge and experience of the three main “stakeholder” communities: researchers, clinicians and patients/carers. The ability to harness such data is essential for the integration of medical information to support clinical decision making and medical research.


Medical Knowledge Text Mining Clinical Decision Support Clinical Decision Support System Biomedical Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Farr Institute of Health Informatics ResearchHealth eResearch CentreManchesterUK
  2. 2.School of Computer ScienceUniversity of ManchesterManchesterUK

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