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Standard NER Tagging Scheme for Big Data Healthcare Analytics built on Unified medical corpora

Journal of Artificial Intelligence and Technology 2022年第4期2卷 152-157页

作者： Sarah Shafqat Hammad Majeed Qaisar Javaid Hafiz Farooq Ahmad Department of basic and Applied Sciences International Islamic University(IIU)IslamabadPakistan Department of Computer Science National University of Computer and Emerging SciencesIslamabadPakistan Computer Science Department College of Computer Sciences and Information Technology(CCSIT)King Faisal UniversityAl-Ahsa 31982Saudi Arabia

The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing,recommending,prescribing,or treating patients for uniform phenotype features from patients’*** authors of this paper while searching for possible solutions for medical context learning found that unified corpora tagged with medical nomenclature was missing to train the analytics for medical context ***,here we demonstrated a mechanism to come up with uniform NER(Named Entity Recognition)tagged medical corpora that is fed with 14407 endocrine patients’data set in Comma Separated Values(CSV)format diagnosed with diabetes mellitus and comorbidity *** other corpus is of ICD-10-CM coding scheme in text format taken from ***-10-CM corpus is to be tagged for understanding the medical context with uniformity for which we are conducting different experiments using common natural language programming(NLP)techniques and frameworks like TensorFlow,Keras,Long Short-Term Memory(LSTM),and *** our preliminary experiments,albeit label sets in form of(instance,label)pair were tagged with Sequential()model formed on *** and Bi-LSTM NLP *** maximum accuracy achieved for model validation was 0.8846.

关键词： big data endocrine diseases international diabetes federation healthcare analytics ICD-10 medical corpora NLP

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