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TachyHealth to Present Publication at ACLing 2021 Virtual Conference

May 27, 2021 · AI, NLP, Publication,
medical image

TachyHealth is happy to announce that our team publication “Med-Flair: medical named entity recognition for diseases and medications based on Flair embedding” will be presented at the ACLing 2021: 5th International Conference on AI in Computational Linguistics Virtual Conference, on Friday June 4, 2021.

The ACLing conference brings together world leading academicians, scientists, researchers and practitioners to exchange the latest ideas and results in Computational Linguistics and NLP. The scope of the conference encompasses the theory and practice of all aspects of AI in Computational Linguistics with regard to text, audio, image, and video. This year, the conference will be organized and hosted by The British University in Dubai due to its established mission to become a provider of world class scholarship, education, and research.

Named Entity Recognition (NER) is a vital step in medical information extraction, especially Electronic Health Records (EHRs). Proper extraction of medical entities such as disease and medications can automate the process of EHR coding as well as considerably improve the filtering of EHR resulting in better extraction of medical information. NER systems are generally trained and evaluated on relatively small standard datasets. However, they are applied on real-world applications, they are exposed to different collection of texts, varying in topic, entity distribution, and text type (e.g. abstract vs. full text). This mismatch between the internal structure and application can cause drop in performance and consequently, unreliability.

In this paper, we propose Med-Flair, an NER tagger covering mainly multiple entity types, medications and diseases. Med-Flair is mainly based on the Flair NLP framework, in addition, it's integrated by adding Bidirectional A Long Short Term Memory network (BiLSTM) and Conditional Random Fields (CRF) for sequence tagger. To validate the performance of Med-Flair, it is tested on 4 benchmark datasets, two for medications entities and two for diseases entities. Med Flair successfully achieves high performance, as it achieves 92\%, 88\% , 92\% and 95\% of F1-score which are mostly highest compared to state of the art deep neural network architectures such as BioBERT, DTranNER, BERT and BioNerFlair.

TachyHealth is leading using AI and data science to transform healthcare with deep technology solving challenging problems grounded on reality. It aims to save lives with tech by driving the focus of healthcare on what really matters, the patients, and in doing so, taking away all the waste, redundancies, and inefficiencies with value-driven healthcare solutions for both healthcare providers and payers.