A framework for named entity recognition of clinical data

Ravikumar J, Ramakanth Kumar P


With emergence of technologies like big data, the healthcare services are also being explored to apply this technology and reap benefits. Big Data analytics can be implemented as a part of e-health which involves the extrapolation of actionable insights from sources like health knowledge base and health information systems. Present day medical data creates a lot of information consistently. At present, Hospital Information System is a quickly developing innovation. This data is a major asset for getting data from gathering of gigantic measures of surgical information by forcing a few questions and watchwords. Be that as it may, there is issue of getting data precisely what the client need, because Hospital Information System contains more than one archive identified with a specific thing, individual or episode and so on. Information extraction is one of information mining systems used to concentrate models portraying essential information classes. The proposed work will work for the most part concentrating on accomplishing great execution in Medical Domain. Fundamentally this had two primary purposes one was separating significant information from patient content record and second one labelling name substance, for example, individual, association, area, malady name and symptoms. Improve survival rates and tweak care conventions and review inquiries to better deal with any interminable consideration populace. Lower costs by decreasing pointless hospitalizations. Abbreviate length of stay when confirmation is fundamental.


Information extraction, Named entity recognition, Surgical data

Full Text:



Z. Liu, X. Wang, Q. Chen and B. Tang, "Chinese Clinical Entity Recognition via Attention-Based CNN-LSTM-CRF," IEEE International Conference on Healthcare Informatics Workshop (ICHI-W), New York, NY, 2018, pp. 68-69,2018

Yefeng Wang, Jon Patrick, “Cascading Classifiers for Named Entity Recognition in Clinical Notes”, Workshop Biomedical Information Extraction - Borovets, Bulgaria, pp: 42–49,2009.

Zengjian Liu, Ming Yang, Xiaolong Wang, “Entity re cognition from clinical texts via recurrent neural network”,

BMC Medical Informatics and Decision Making 17(S2).

Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg, Alejo Nevado-Holgado, “Few-shot Learning for Named

Entity Recognition in Medical Text”, arXiv:1811.05468,pp:1-10,NOV 2018.

T. Joachims, C. Nedellec, and C. Rouveirol. “Text categorization with support vector machines: learning with many

relevant. In Machine Learning”: ECML-98 10th European Conference on Machine Learning, Chemnitz, Germany.

Springer, pp:137-142,1998.

L. Rabiner et al. “A tutorial on hidden Markov models and selected applications in speech recognition”.Proceedings

of the IEEE,vol. 77(2),pp:257–286, 1989.

S.M. Meystre, G.K. Savova, K.C. Kipper-Schuler, J.F. Hurdle, “Extracting informa-tion from textual documents in

the electronic health record”: a review of recentresearch, Yearb Med. Inform. 35,pp:128-144,2008.

R.W.V. Flynn, T.M. Macdonald, N. Schembri, G.D. Murray, A.S.F. Doney, “Automated data capture from free-text

radiology reports to enhance accuracy of hospital inpatient stroke codes”,Pharmacoepidemiol. Drug Saf. 19


H. Yang, I. Spasic, J.A. Keane, G. Nenadic, “A text mining approach to the pre-diction of disease status from

clinical discharge summaries”, J. Am. Med. Inform.Assoc, pp:596–600,2009.

Sekine, S. 1998. “Nyu: Description of the Japanese NE System Used For Met-2”. In Proc. Message Understanding


DOI: http://doi.org/10.11591/ijeecs.v18.i2.pp946-952
Total views : 72 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics