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Text Mining Supporting Search for Knowledge Discovery in Diabetes

Author:
Ananiadou, Sophia   Ohta, Tomoko   Rutter, Martin K.  


Journal:
Current Cardiovascular Risk Reports


Issue Date:
2013


Abstract(summary):

Due to increasing specialization, silo effects and literature deluge, researchers are struggling to draw out general truths and to generate testable hypotheses. This is especially true when considering the needs of biomedicine. Medicine faces many challenges, not least the fragmentation into multiple subspecialist areas, and, at the same time, the need for cutting-edge research to be interdisciplinary. There are also issues of communication and understanding between those working at different ‘-omics levels’ and those working in a myriad of diverse areas including: basic research, translational medicine, clinical care, clinical trials, epidemiology, public health, clinical guideline development, evaluation of new drugs and treatments and personalized medicine. Most importantly, there is a lack of effective communication between these groups and members of the general public who seek to become better informed about their health. Different people have different views, perspectives and information needs relating to the same topic. Text mining methods can support information access for diverse groups such as researchers, clinicians, caregivers, patients and also members of the general public.


Page:
1-8


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