Named entity recognition on bio-medical literature documents using hybrid based approach

Abstract

There have been many changes in the medical field due to technological advances. The progression in technologies provides lot of opportunities to extract valuable insights from huge amount of unstructured data. The literature documents published by the researchers in medical domain consists enormous amount of knowledge. Many organizations are involving in retrieving the hidden information from the literature documents. Extracting the drug names, diseases, symptoms, route of administration, species and dosage forms from the textual document is an easy task due to the innovation of technologies in the Natural Language Processing. In this article, a new hybrid based approach is proposed to identify named entity from the medical literature documents. New dictionary has been built for route of administration, dosage forms and symptoms to annotate the entities in the medical documents. The annotated entities are trained by the blank Spacy machine learning model. The trained model provide a decent accuracy when compared with the existing model. The hybrid model is validated with the dictionary and human (optional)to calculate the confusion matrix. It is able to identify more entities than the prevailing model. The average F1 score for five entities of the proposed hybrid based approach 73.79%.

Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature

“…The methodologies applied in BioNER have evolved over the years to reach the current state of the art, which is mainly based on the use of language models (e.g. BERT) that are pre-trained in the biomedical domain to specify their underlying knowledge [29] , [30] , [31] .…”

Artificial Intelligence and Cardiovascular Genetics

“…Within genomics, NLP has been used for gene recognition or normalization [100] and identifying gene-disease associations in heart failure [101]. Interestingly, NLP has also been used to predict genes for CAD [102,103], while other techniques rely on a combination of ML, DL, and NLP to predict gene alterations [63,64].…”

“…These AI approaches fundamentally work to train programs to recognize relationships within data. NLP Gene regulation network SpaCy [63] NLP Tagging, parsing, and entity recognition…”

Tlbioner: Transfer Learning Based Named Entity Recognition on Medical Literature Documents

“…The role of AI in NLP makes the developers to extract the structure information from the big data. Several libraries are being developed in NLP for identifying the important keywords in the EMR [Ramachandran R and Arutchelvan K (2021)]. Due to the advancement of research and discoveries in the life science domain, huge number of applications are being introduced for various analysis.…”

“…Precision: It is the fraction of correctly classified positive label (i.e., True Positive (TP)) with the all-positive labels (TP, False Positive (FP)) obtained from the datasets. The equation is given in the equation(1). It is the fraction of TP with the correctly classified labels and incorrectly classified labels (False Negative (FN)).…”