Open AccessA framework for named entity recognition of clinical data
Author(s) -
J Ravikumar,
Ramakanth Kumar P
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v18.i2.pp946-952
Subject(s) - big data , asset (computer security) , data science , computer science , analytics , health care , information system , domain (mathematical analysis) , work (physics) , information extraction , knowledge management , data mining , information retrieval , computer security , engineering , mechanical engineering , mathematical analysis , mathematics , electrical engineering , economics , economic growth
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.