Open AccessENTITY EXTRACTION FROM UNSTRUCTURED MEDICAL TEXT
Author(s) -
G Deepank,
R Tharun Raj,
Aditya Verma
Publication year - 2020
Publication title -
international journal of engineering applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-2143
DOI - 10.33564/ijeast.2020.v05i08.041
Subject(s) - bespoke , swift , computer science , medical record , field (mathematics) , information extraction , key (lock) , electronic medical record , artificial intelligence , data science , information retrieval , medicine , internet privacy , radiology , mathematics , computer security , political science , pure mathematics , law , programming language
Electronic medical records represent richdata repositories loaded with valuable patientinformation. As artificial intelligence and machinelearning in the field of medicine is becoming more popularby the day, ways to integrate it are always changing. Onesuch way is processing the clinical notes and records,which are maintained by doctors and other medicalprofessionals.Natural language processing can record this data andread more deeply into it than any human. Deep learningtechniques such as entity extraction which involvesidentifying and returning of key data elements from anelectronic medical record, and other techniques involvingmodels such as BERT for question answering, whenapplied to all these medical records can create bespokeand efficient treatment plans for the patients, which can help in a swift and carefree recovery.